Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

These results are important because segmentation with DL makes photogrammetry for limb scanning feasible on a large clinical scale.

The review comment to be evaluated is:

This paper proposes a method using deep learning with photogrammetry for scanning amputated limbs. The proposed method uses deep convolutional neural networks, DeepLabv3, and transfer learning. The ResNet-101-based Deep Labv3 model was pre-trained and then fine-tuned. The scan images were acquired by using a smartphone and subsequently segmented on a desktop. Afterward, 3D limb models were rendered from the segmented images using Agisoft Metashape.

The evidence to be evaluated is:The development of digital cameras and the accompanying transition to digital photogrammetry has led to technologies for the reconstruction of 3D models using photogrammetric algorithms Linder (2016). Digital photogrammetry has been used successfully to reconstruct patient anatomy in many medical contexts such as cranial deformation scanning Barbero-Garcia et al. (2017, 2020, 2021), facial scanning Ross et al. (2018); Nightingale et al. (2020, 2021), and amputated limb scanning R. B. Taqriban et al. (2019); Cabrera et al. (2020); Ismail et al. (2020); De Vivo Nicoloso et al. (2021).

Close range photogrammetric approaches have been proven to have accuracy comparable to clinical gold standard technologies Ross et al. (2018).

For this reason, recent research has focused on various methods for automating photogrammetric workflows for anatomical scanning Barbero-Garcia et al. (2020); Cabrera et al. (2020).

Automated approaches have focused their attention on this acquisition stage to ensure completeness of the results Nocerino et al. (2017) with recent advances incorporating machine learning for landmark detection Barbero-Garcia et al. (2021). Still, automation of photogrammetric image processing (specifically image segmentation) remains a large problem Cabrera et al. (2021). Automating this image segmentation step could dramatically increase the speed of photogrammetric workflows for medical imaging, improving the clinical viability. Medical image segmentation plays an essential role in modern clinical practice by facilitating computer aided diagnoses and making patient anatomical structures clear Wang et al. (2022).

Recent research has shown great success in using transfer learning with the DeepLabv3 architecture for medical imaging segmentation Roy et al. (2020). We performed transfer learning on a pre-trained resnet-101 based DeepLabv3 model downloaded from the Pytorch Model Zoo Paszke et al. (2019). This model was pre-trained on the COCO train2017 data set comprising 200,000 images and 80 object categories Lin et al. (2015). Our fine-tuning dataset consisted of 806 manually labeled images of limbs with transtibial amputations.

We captured 24 limb scans to test the applicability of the neural network model for image segmentation. 12 scans were performed with a single transtibial amputee wearing a standard liner then 12 additional scans were performed with the transtibial amputee wearing a colored sock over their standard liner. Scans were performed utilizing the Lim(b)itless smartphone application Cabrera et al. (2020) on a Motorola Moto G7 (MSRP $299, Camera - 12 MP, f/1.8, 1/2.8") see Fig. 1. The scanning procedure involved making two revolutions (one clockwise, one counterclockwise) around the outside of the limb while keeping the limb in frame of the application. Scans were taken at a constant radius of 30 cm from the limb, similar to Barbero-Garcia et al. (2018), at a rotation rate of two revolutions per minute. This application automatically initiates a capture sequence at 10 degree intervals with each capture sequence including a burst of 3 photographs.

The remaining 22 scans, totaling 6312 images, were fed into the fine-tuned neural network for segmentation. Segmentation time using the neural network averaged 890 s \(\pm\) 111 s, Table 1. 3D limb models were rendered from these segmented images using Agisoft Metashape, Fig. 2.

After 10 epochs of training, the fine tuned neural network showed excellent performance in segmenting images of transtibial amputations, see Fig. 4.

However, the testing IoU remains largely constant, with a value of 79.9% after 10 epochs of training. This IoU is comparable with values reported by Chen et al. (2017b) which reached peak mIoU values of 81.3% on the Cityscapes test set and 85.7% on the PASCAL VOC 2012 test set with the DeepLabv3 architecture.

Increasing the number of images in the training dataset is feasible, but would require scanning additional persons with limb loss and manual segmentation. As with other forms of medical imaging, limited data quantities and expert annotations present hurdles for deep learning techniques Wang et al. (2018). On the other hand, reducing the image downsampling could improve the model performance without requiring additional annotations.

The average MAE values obtained are excellent (0.57 mm \(\pm\) 0.63 mm) and comparable with the sub-millimeter MAE accuracy reported by Barbero-Garcia et al. (2020).

However the use of the colored sock improved tie point detection and image alignment leading to a successful render rate of 90.9 % for the colored sock vs 72.7% for the bare liner.

Failure was primarily due to failed photo alignment which led to incomplete and/or distorted models.

This is important because poor surface finish can be directly tied to the quality of image segmentation, with better segmentation leading to smoother finishes.

Even without Laplacian smoothing, this model shows remarkable rendering accuracy in comparison with the ground truth. Since the RMSE errors are systematically impacted by limb flexure, MAE is the more appropriate measure of accuracy in these scans. For the purposes of rendering residual limb models, sub-millimeter accuracy presents little meaningful advantage to clinicians and practitioners. Unlike the skulls studied by Barbero-Garcia et al. (2017, 2020), amputated limbs undergo constant shape and deformation changes due to effects of donning-doffing, muscle contractions, interstitial fluid movements etc.

The advances in photogrammetric limb scanning resulting from automation of the segmentation step could have potentially significant consequences. Notably, automatic segmentation decreases the amount of human time and effort required to render photogrammetric models. As mentioned previously in the methods, a limb scan captured via the Lim(b)ittless smartphone application captures at least 216 images. Manual segmentation of those images would take (at minimum) 1 minute per image leading to a segmentation time of 12,960 seconds. The fine-tuned network trained in this paper would only take 675 seconds to perform this task based on the segmentation rate reported in Table 1.

Evaluating the end to end process using the rate values in Table 1 reveals that the model rendering workflow is 4.88x faster overall. Although the process bottleneck shifts from segmentation rate to rendering rate, the only step requiring significant human input is the scanning stage which takes less than 1 minute on average, Table 1. Models from the updated photogrammetric workflow could be used in a variety of contexts, since there was no significant geometrical difference between ground truth models and models rendered from images segmented by the fine-tuned neural network. Most notably, photogrammetric models built from smartphone scans can facilitate a host of low-cost telemedicine solutions. As one example, the limb models could be utilized as the first stage of digital prosthetic socket rectification. This would enable patients to be able to be scanned outside of a clinical environment (eg. at home) and have a prosthesis fabricated digitally and shipped to them.

Photogrammetric limb scanning could also facilitate long-term and large scale studies of limb shape and size fluctuations over time. Being able to capture data outside of a clinical environment could help clinicians to increase the number of data points and the frequency of measurements beyond what is currently possible.

This research sought to address limitations in photogrammetric workflows by automating image segmentation via a fine-tuned deep learning model.

After 10 epochs of transfer learning, the fine-tuned deep learning model was able to achieve an IoU of 79.9% on the testing training set.

Applying this fine-tuned model to segment a dataset of 22 scans containing 6312 images showed that there was no significant geometric difference compared to scans rendered from manually segmented images.

Using the fine-tuned model increased the rate of image segmentation by 19.2x as well as sped up the entire photogrammetric workflow by a factor of 4.88x.

This fine-tuned deep learning model can help facilitate large scale image processing for telemedicine applications. Using smartphones to acquire 3D scans of amputated limbs, clinicians could monitor patients, conduct large scale research studies, and even build prostheses remotely. This could potentially increase the standard of care for persons with limb loss who do not live near a clinic by matching patients with clinicians seeking to expand their geographic service area. \begin{table}
\begin{tabular}{l l} \hline
**Number of Images** & 287 \(\pm\) 29 \\
**Scan Time (s)** & 52.4 \(\pm\) 6.1 \\
**Segmentation Time (s)** & 890 \(\pm\) 111 \\
**Render Time (s)** & 3430 \(\pm\) 850 \\
**Scan Rate (images/s)** & 5.54 \(\pm\) 0.74 \\
**Segmentation Rate (images/s)** & 0.32 \(\pm\) 0.01 \\
**Render Rate (images/s)** & 0.088 \(\pm\) 0.022 \\ \hline \end{tabular}
\end{table}
Table 1: Image acquisition and rendering pipeline statistics for smartphone scans (n = 22) used in this study.

Input: Table 1: Image acquisition and rendering pipeline statistics for smartphone scans (n = 22) used in this study.

Input: Figure 4: Limbs with transtibial amputations used in the transfer learning process. A) These limb photographs were taken in a variety of lighting conditions and with different liners. B) Manually segmented binary masks representing the ground truth. C) Masks generated by the fine-tuned neural network. D) Applying the masks from the fine-tuned neural network to the original images.

Input: Figure 5: Selected comparison of limbs in CloudCompare. In these 3D scans, it is evident that the large RMSE is due to a change in geometry from limb flexure, and is not due to segmentation or render quality. A) Ground truth model rendered from manually segmented images. B) Scan 12 - Highest RMSE (5.54 mm) evident limb flexure C) Scan 13 - Low RMSE (1.26 mm) no limb flexure D) Scan 16 - Medium RMSE (3.31 mm) evident limb flexure E) Scan 17 - Medium RMSE (2.71 mm) evident surface noise and limb flexure.

Input: Figure 1: Scanning a Transtibial Amputee using the Lim(b)itless Cell Phone Application

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the evidence, I'll evaluate the claim "These results are important because segmentation with DL makes photogrammetry for limb scanning feasible on a large clinical scale."

Summary:
The evidence strongly supports this claim. The research demonstrates:
- 19.2x faster segmentation speed compared to manual methods
- 4.88x faster overall workflow
- Comparable accuracy to manual segmentation (IoU 79.9%)
- Sub-millimeter accuracy (MAE 0.57mm ± 0.63mm)
- Significantly reduced human input time (only ~1 minute scanning required)
- Successful implementation with basic smartphone hardware

While there are some limitations (90.9% success rate with colored sock, 72.7% with bare liner), the evidence convincingly shows that DL segmentation makes the process clinically feasible through dramatic time savings and maintained accuracy.

<score>1</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

These results are important because segmentation with DL makes photogrammetry for limb scanning feasible on a large clinical scale.

The review comment to be evaluated is:

The problem statement is clearly explained and the method addresses an important problem of improving time and accuracy of healthcare procedures. A very detailed discussion of clinical impact and directions is presented at the end of the work, what helps with understanding potential applications, advantages and limitations of the method. A few ideas for improvements are also presented, showing that authors have a good understanding of the presented medical problem.

Unfortunately, the novelty of the work is limited. Very similar studies using deep learning segmentation topologies for improvements of photogrammetry have already been conducted. The work is an application of a known method to another computer vision dataset, but experiments focus only on a single model, so it may be difficult to draw some conclusions for future enhancements of such methods.

Also, the reason for selection of the specific DeepLab model hasn't been justified. It would be interesting to verify other models as well.

It's also mentioned that photo alignment led to some failure cases. There are various alignment techniques that could be used to improve this outcome. Have you tried any of them?

The evidence to be evaluated is:The development of digital cameras and the accompanying transition to digital photogrammetry has led to technologies for the reconstruction of 3D models using photogrammetric algorithms Linder (2016). Digital photogrammetry has been used successfully to reconstruct patient anatomy in many medical contexts such as cranial deformation scanning Barbero-Garcia et al. (2017, 2020, 2021), facial scanning Ross et al. (2018); Nightingale et al. (2020, 2021), and amputated limb scanning R. B. Taqriban et al. (2019); Cabrera et al. (2020); Ismail et al. (2020); De Vivo Nicoloso et al. (2021).

Close range photogrammetric approaches have been proven to have accuracy comparable to clinical gold standard technologies Ross et al. (2018).

For this reason, recent research has focused on various methods for automating photogrammetric workflows for anatomical scanning Barbero-Garcia et al. (2020); Cabrera et al. (2020).

Automated approaches have focused their attention on this acquisition stage to ensure completeness of the results Nocerino et al. (2017) with recent advances incorporating machine learning for landmark detection Barbero-Garcia et al. (2021). Still, automation of photogrammetric image processing (specifically image segmentation) remains a large problem Cabrera et al. (2021). Automating this image segmentation step could dramatically increase the speed of photogrammetric workflows for medical imaging, improving the clinical viability. Medical image segmentation plays an essential role in modern clinical practice by facilitating computer aided diagnoses and making patient anatomical structures clear Wang et al. (2022).

Recent research has shown great success in using transfer learning with the DeepLabv3 architecture for medical imaging segmentation Roy et al. (2020). We performed transfer learning on a pre-trained resnet-101 based DeepLabv3 model downloaded from the Pytorch Model Zoo Paszke et al. (2019). This model was pre-trained on the COCO train2017 data set comprising 200,000 images and 80 object categories Lin et al. (2015). Our fine-tuning dataset consisted of 806 manually labeled images of limbs with transtibial amputations.

We captured 24 limb scans to test the applicability of the neural network model for image segmentation. 12 scans were performed with a single transtibial amputee wearing a standard liner then 12 additional scans were performed with the transtibial amputee wearing a colored sock over their standard liner. Scans were performed utilizing the Lim(b)itless smartphone application Cabrera et al. (2020) on a Motorola Moto G7 (MSRP $299, Camera - 12 MP, f/1.8, 1/2.8") see Fig. 1. The scanning procedure involved making two revolutions (one clockwise, one counterclockwise) around the outside of the limb while keeping the limb in frame of the application. Scans were taken at a constant radius of 30 cm from the limb, similar to Barbero-Garcia et al. (2018), at a rotation rate of two revolutions per minute. This application automatically initiates a capture sequence at 10 degree intervals with each capture sequence including a burst of 3 photographs.

The remaining 22 scans, totaling 6312 images, were fed into the fine-tuned neural network for segmentation. Segmentation time using the neural network averaged 890 s \(\pm\) 111 s, Table 1. 3D limb models were rendered from these segmented images using Agisoft Metashape, Fig. 2.

After 10 epochs of training, the fine tuned neural network showed excellent performance in segmenting images of transtibial amputations, see Fig. 4.

However, the testing IoU remains largely constant, with a value of 79.9% after 10 epochs of training. This IoU is comparable with values reported by Chen et al. (2017b) which reached peak mIoU values of 81.3% on the Cityscapes test set and 85.7% on the PASCAL VOC 2012 test set with the DeepLabv3 architecture.

Increasing the number of images in the training dataset is feasible, but would require scanning additional persons with limb loss and manual segmentation. As with other forms of medical imaging, limited data quantities and expert annotations present hurdles for deep learning techniques Wang et al. (2018). On the other hand, reducing the image downsampling could improve the model performance without requiring additional annotations.

The average MAE values obtained are excellent (0.57 mm \(\pm\) 0.63 mm) and comparable with the sub-millimeter MAE accuracy reported by Barbero-Garcia et al. (2020).

However the use of the colored sock improved tie point detection and image alignment leading to a successful render rate of 90.9 % for the colored sock vs 72.7% for the bare liner.

Failure was primarily due to failed photo alignment which led to incomplete and/or distorted models.

This is important because poor surface finish can be directly tied to the quality of image segmentation, with better segmentation leading to smoother finishes.

Even without Laplacian smoothing, this model shows remarkable rendering accuracy in comparison with the ground truth. Since the RMSE errors are systematically impacted by limb flexure, MAE is the more appropriate measure of accuracy in these scans. For the purposes of rendering residual limb models, sub-millimeter accuracy presents little meaningful advantage to clinicians and practitioners. Unlike the skulls studied by Barbero-Garcia et al. (2017, 2020), amputated limbs undergo constant shape and deformation changes due to effects of donning-doffing, muscle contractions, interstitial fluid movements etc.

The advances in photogrammetric limb scanning resulting from automation of the segmentation step could have potentially significant consequences. Notably, automatic segmentation decreases the amount of human time and effort required to render photogrammetric models. As mentioned previously in the methods, a limb scan captured via the Lim(b)ittless smartphone application captures at least 216 images. Manual segmentation of those images would take (at minimum) 1 minute per image leading to a segmentation time of 12,960 seconds. The fine-tuned network trained in this paper would only take 675 seconds to perform this task based on the segmentation rate reported in Table 1.

Evaluating the end to end process using the rate values in Table 1 reveals that the model rendering workflow is 4.88x faster overall. Although the process bottleneck shifts from segmentation rate to rendering rate, the only step requiring significant human input is the scanning stage which takes less than 1 minute on average, Table 1. Models from the updated photogrammetric workflow could be used in a variety of contexts, since there was no significant geometrical difference between ground truth models and models rendered from images segmented by the fine-tuned neural network. Most notably, photogrammetric models built from smartphone scans can facilitate a host of low-cost telemedicine solutions. As one example, the limb models could be utilized as the first stage of digital prosthetic socket rectification. This would enable patients to be able to be scanned outside of a clinical environment (eg. at home) and have a prosthesis fabricated digitally and shipped to them.

Photogrammetric limb scanning could also facilitate long-term and large scale studies of limb shape and size fluctuations over time. Being able to capture data outside of a clinical environment could help clinicians to increase the number of data points and the frequency of measurements beyond what is currently possible.

This research sought to address limitations in photogrammetric workflows by automating image segmentation via a fine-tuned deep learning model.

After 10 epochs of transfer learning, the fine-tuned deep learning model was able to achieve an IoU of 79.9% on the testing training set.

Applying this fine-tuned model to segment a dataset of 22 scans containing 6312 images showed that there was no significant geometric difference compared to scans rendered from manually segmented images.

Using the fine-tuned model increased the rate of image segmentation by 19.2x as well as sped up the entire photogrammetric workflow by a factor of 4.88x.

This fine-tuned deep learning model can help facilitate large scale image processing for telemedicine applications. Using smartphones to acquire 3D scans of amputated limbs, clinicians could monitor patients, conduct large scale research studies, and even build prostheses remotely. This could potentially increase the standard of care for persons with limb loss who do not live near a clinic by matching patients with clinicians seeking to expand their geographic service area. \begin{table}
\begin{tabular}{l l} \hline
**Number of Images** & 287 \(\pm\) 29 \\
**Scan Time (s)** & 52.4 \(\pm\) 6.1 \\
**Segmentation Time (s)** & 890 \(\pm\) 111 \\
**Render Time (s)** & 3430 \(\pm\) 850 \\
**Scan Rate (images/s)** & 5.54 \(\pm\) 0.74 \\
**Segmentation Rate (images/s)** & 0.32 \(\pm\) 0.01 \\
**Render Rate (images/s)** & 0.088 \(\pm\) 0.022 \\ \hline \end{tabular}
\end{table}
Table 1: Image acquisition and rendering pipeline statistics for smartphone scans (n = 22) used in this study.

Input: Table 1: Image acquisition and rendering pipeline statistics for smartphone scans (n = 22) used in this study.

Input: Figure 4: Limbs with transtibial amputations used in the transfer learning process. A) These limb photographs were taken in a variety of lighting conditions and with different liners. B) Manually segmented binary masks representing the ground truth. C) Masks generated by the fine-tuned neural network. D) Applying the masks from the fine-tuned neural network to the original images.

Input: Figure 5: Selected comparison of limbs in CloudCompare. In these 3D scans, it is evident that the large RMSE is due to a change in geometry from limb flexure, and is not due to segmentation or render quality. A) Ground truth model rendered from manually segmented images. B) Scan 12 - Highest RMSE (5.54 mm) evident limb flexure C) Scan 13 - Low RMSE (1.26 mm) no limb flexure D) Scan 16 - Medium RMSE (3.31 mm) evident limb flexure E) Scan 17 - Medium RMSE (2.71 mm) evident surface noise and limb flexure.

Input: Figure 1: Scanning a Transtibial Amputee using the Lim(b)itless Cell Phone Application

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
Let me assess the claim "These results are important because segmentation with DL makes photogrammetry for limb scanning feasible on a large clinical scale."

The evidence shows significant improvements in efficiency (19.2x faster segmentation, 4.88x faster overall workflow) with comparable accuracy to manual methods (IoU of 79.9%, sub-millimeter MAE). However, there are important limitations:

1. Only one DL model was tested (noted in review)
2. Image alignment issues persisted (72.7-90.9% success rate)
3. Limited dataset size (806 training images, 22 test scans)
4. Single participant testing
5. No clinical validation at scale

While the results are promising, claiming large-scale clinical feasibility is somewhat overstated given these limitations and lack of multi-center validation.

<score>3</score>

The claim would be better stated by acknowledging these limitations while highlighting the potential for scaling based on the efficiency gains demonstrated.


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

We hypothesize that automating image segmentation via DeepLabv3 then rendering the segmented images using photogrammetry could create an efficient processing pipeline for scanning amputated limbs with smartphones.

The evidence to be evaluated is:In this context, photogrammetry has been used extensively since the 1950's for medical imaging and measurementNewton & Mitchell (1996). The development of digital cameras and the accompanying transition to digital photogrammetry has led to technologies for the reconstruction of 3D models using photogrammetric algorithms Linder (2016). Digital photogrammetry has been used successfully to reconstruct patient anatomy in many medical contexts such as cranial deformation scanning Barbero-Garcia et al. (2017, 2020, 2021), facial scanning Ross et al. (2018); Nightingale et al. (2020, 2021), and amputated limb scanning R. B. Taqriban et al. (2019); Cabrera et al. (2020); Ismail et al. (2020); De Vivo Nicoloso et al. (2021).

Close range photogrammetric approaches have been proven to have accuracy comparable to clinical gold standard technologies Ross et al. (2018). Using an Artec Spider structured light scanner as a clinical "gold standard" reference, Nightingale et al. (2020) found that photogrammetric reconstructions of facial scans using 80 images captured had RMSE accuracy values of 1.3 mm \(\pm\) 0.3 mm. In a similar study, Nightingale et al. (2021) achieved RMSE accuracy values of 1.5 mm \(\pm\) 0.4 mm with 30 photographs on reconstructions of the external ear.

For this reason, recent research has focused on various methods for automating photogrammetric workflows for anatomical scanning Barbero-Garcia et al. (2020); Cabrera et al. (2020).

Automated approaches have focused their attention on this acquisition stage to ensure completeness of the results Nocerino et al. (2017) with recent advances incorporating machine learning for landmark detection Barbero-Garcia et al. (2021). Still, automation of photogrammetric image processing (specifically image segmentation) remains a large problem Cabrera et al. (2021). Automating this image segmentation step could dramatically increase the speed of photogrammetric workflows for medical imaging, improving the clinical viability. Medical image segmentation plays an essential role in modern clinical practice by facilitating computer aided diagnoses and making patient anatomical structures clear Wang et al. (2022). Prior to recent developments in deep learning (DL), computer vision techniques such as k-nearest neighbors (kNN), decision trees (DT), support vector machines (SVM), and random forest (RF) models were utilized for segmentation and classification tasks Thanh Noi and Kappas (2017)

DL has superseded all of these approaches in medical imaging for several reasons, but most notably because the burden of feature engineering in DL shifts from humans to computers Shen et al. (2017). CNNs are the most heavily researched DL architectures for medical image analysis J. Ker et al. (2018).

The DeepLabv3 architecture expands on traditional CNNs by utilizing atrous convolution with spatial pyramid pooling Chen et al. (2017). Atrous convolution utilizes a dilated convolution kernel that, rather than focusing on adjacent pixels, convolves pixels that are not immediate neighbors. Atrous convolution helps to resolve the issue of spatial resolution loss in feature maps from successive convolution and de-convolution steps by allowing for precise control of feature map density. Atrous Spatial Pyramid Pooling (ASPP) Chen et al. (2017) utilizes four parallel atrous convolutions with different atrous rates to transform feature maps. This allows for accurate and efficient classification regions at arbitrary scales Chen et al. (2017). These elements of the DeepLabv3 architecture result in better classification of pixels for semantic image segmentation, on par with other state-of-the-art models.

Recent research has shown great success in using transfer learning with the DeepLabv3 architecture for medical imaging segmentation Roy et al. (2020). We performed transfer learning on a pre-trained resnet-101 based DeepLabv3 model downloaded from the Pytorch Model Zoo Paszke et al. (2019). This model was pre-trained on the COCO train2017 data set comprising 200,000 images and 80 object categories Lin et al. (2015). Our fine-tuning dataset consisted of 806 manually labeled images of limbs with transtibial amputations.

Following this, we randomly select 80% of the images as our training data set and reserve the remaining 20% for validation.

We captured 24 limb scans to test the applicability of the neural network model for image segmentation. 12 scans were performed with a single transtibial amputee wearing a standard liner then 12 additional scans were performed with the transtibial amputee wearing a colored sock over their standard liner. Scans were performed utilizing the Lim(b)itless smartphone application Cabrera et al. (2020) on a Motorola Moto G7 (MSRP $299, Camera - 12 MP, f/1.8, 1/2.8") see Fig. 1. The scanning procedure involved making two revolutions (one clockwise, one counterclockwise) around the outside of the limb while keeping the limb in frame of the application. Scans were taken at a constant radius of 30 cm from the limb, similar to Barbero-Garcia et al. (2018), at a rotation rate of two revolutions per minute. This application automatically initiates a capture sequence at 10 degree intervals with each capture sequence including a burst of 3 photographs.

The remaining 22 scans, totaling 6312 images, were fed into the fine-tuned neural network for segmentation. Segmentation time using the neural network averaged 890 s \(\pm\) 111 s, Table 1. 3D limb models were rendered from these segmented images using Agisoft Metashape, Fig. 2.

Finally, STL meshes of the scans rendered from automatically segmented images were compared with scans rendered from ground truth images via the CloudCompare open source 3D point cloud and mesh processing software.

After 10 epochs of training, the fine tuned neural network showed excellent performance in segmenting images of transtibial amputations, see Fig. 4.

However, the testing IoU remains largely constant, with a value of 79.9% after 10 epochs of training. This IoU is comparable with values reported by Chen et al. (2017b) which reached peak mIoU values of 81.3% on the Cityscapes test set and 85.7% on the PASCAL VOC 2012 test set with the DeepLabv3 architecture.

Increasing the number of images in the training dataset is feasible, but would require scanning additional persons with limb loss and manual segmentation. As with other forms of medical imaging, limited data quantities and expert annotations present hurdles for deep learning techniques Wang et al. (2018). On the other hand, reducing the image downsampling could improve the model performance without requiring additional annotations. In Fig. 4 it can be observed that the masks generated by the neural network are partially limited in accuracy due to this downsampling.

This is important because poor surface finish can be directly tied to the quality of image segmentation, with better segmentation leading to smoother finishes.

Unlike the skulls studied by Barbero-Garcia et al. (2017, 2020), amputated limbs undergo constant shape and deformation changes due to effects of donning-doffing, muscle contractions, interstitial fluid movements etc.

The advances in photogrammetric limb scanning resulting from automation of the segmentation step could have potentially significant consequences. Notably, automatic segmentation decreases the amount of human time and effort required to render photogrammetric models. As mentioned previously in the methods, a limb scan captured via the Lim(b)ittless smartphone application captures at least 216 images. Manual segmentation of those images would take (at minimum) 1 minute per image leading to a segmentation time of 12,960 seconds. The fine-tuned network trained in this paper would only take 675 seconds to perform this task based on the segmentation rate reported in Table 1.

Evaluating the end to end process using the rate values in Table 1 reveals that the model rendering workflow is 4.88x faster overall.

Models from the updated photogrammetric workflow could be used in a variety of contexts, since there was no significant geometrical difference between ground truth models and models rendered from images segmented by the fine-tuned neural network. Most notably, photogrammetric models built from smartphone scans can facilitate a host of low-cost telemedicine solutions. As one example, the limb models could be utilized as the first stage of digital prosthetic socket rectification. This would enable patients to be able to be scanned outside of a clinical environment (eg. at home) and have a prosthesis fabricated digitally and shipped to them. Aside from this, one distinct advantage of the photogrammetric process is it retains surface features which can be reprojected onto the final 3D model. With UV texture mapping, doctors could get a 3D view of limbs and look closely at their outer surfaces (such as for lesions) in virtual reality (VR).

Being able to capture data outside of a clinical environment could help clinicians to increase the number of data points and the frequency of measurements beyond what is currently possible.

On a technical level, the fine-tuned segmentation model from this research would need to be evaluated for robustness. Obtaining more scans of different skin colors, different lighting environments, etc. while training the model further could improve the robustness and reduce the risk of bias. It remains to be seen whether this model could be applied to segment different types of amputation or if the model would need to be trained on additional examples.

This research sought to address limitations in photogrammetric workflows by automating image segmentation via a fine-tuned deep learning model.

Applying this fine-tuned model to segment a dataset of 22 scans containing 6312 images showed that there was no significant geometric difference compared to scans rendered from manually segmented images. The MAE was found to be on average 0.57 mm \(\pm\) 0.63 mm while the RMSE was found to be on average 2.43 mm \(\pm\) 1.10 mm. The error reported was mostly influenced by slight changes in limb shape between scans and not render noise caused by image segmentation.

Using the fine-tuned model increased the rate of image segmentation by 19.2x as well as sped up the entire photogrammetric workflow by a factor of 4.88x.

This fine-tuned deep learning model can help facilitate large scale image processing for telemedicine applications. Using smartphones to acquire 3D scans of amputated limbs, clinicians could monitor patients, conduct large scale research studies, and even build prostheses remotely. This could potentially increase the standard of care for persons with limb loss who do not live near a clinic by matching patients with clinicians seeking to expand their geographic service area. \begin{table}
\begin{tabular}{l l} \hline
**Number of Images** & 287 \(\pm\) 29 \\
**Scan Time (s)** & 52.4 \(\pm\) 6.1 \\
**Segmentation Time (s)** & 890 \(\pm\) 111 \\
**Render Time (s)** & 3430 \(\pm\) 850 \\
**Scan Rate (images/s)** & 5.54 \(\pm\) 0.74 \\
**Segmentation Rate (images/s)** & 0.32 \(\pm\) 0.01 \\
**Render Rate (images/s)** & 0.088 \(\pm\) 0.022 \\ \hline \end{tabular}
\end{table}
Table 1: Image acquisition and rendering pipeline statistics for smartphone scans (n = 22) used in this study.

Input: Table 1: Image acquisition and rendering pipeline statistics for smartphone scans (n = 22) used in this study.

Input: Figure 4: Limbs with transtibial amputations used in the transfer learning process. A) These limb photographs were taken in a variety of lighting conditions and with different liners. B) Manually segmented binary masks representing the ground truth. C) Masks generated by the fine-tuned neural network. D) Applying the masks from the fine-tuned neural network to the original images.

Input: Figure 5: Selected comparison of limbs in CloudCompare. In these 3D scans, it is evident that the large RMSE is due to a change in geometry from limb flexure, and is not due to segmentation or render quality. A) Ground truth model rendered from manually segmented images. B) Scan 12 - Highest RMSE (5.54 mm) evident limb flexure C) Scan 13 - Low RMSE (1.26 mm) no limb flexure D) Scan 16 - Medium RMSE (3.31 mm) evident limb flexure E) Scan 17 - Medium RMSE (2.71 mm) evident surface noise and limb flexure.

Input: Figure 1: Scanning a Transtibial Amputee using the Lim(b)itless Cell Phone Application

Input: Figure 3: Evolution of IoU and loss function for training and testing dataset

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After careful review, the claim appears well-stated and appropriately reflects the available evidence. The research demonstrates:

1. Successful implementation of DeepLabv3 for segmentation (IoU of 79.9%, comparable to benchmark results)
2. Quantified efficiency improvements (19.2x faster segmentation, 4.88x faster overall workflow)
3. Acceptable accuracy (MAE 0.57mm ± 0.63mm, RMSE 2.43mm ± 1.10mm)
4. Clear experimental methodology with 22 scans and 6312 images
5. Appropriate acknowledgment of limitations and future work needed

The hypothesis is conservatively stated as a potential solution rather than making definitive claims, and the evidence thoroughly supports its feasibility.

<score>1</score>

Justification:
1. The claim uses measured language ("could create") rather than making absolute statements
2. All components are directly supported by experimental evidence:
- DeepLabv3 segmentation performance is demonstrated with concrete metrics
- Efficiency gains are quantified in Table 1 and comparative workflow times
- Image quality and accuracy are validated against ground truth
3. Limitations are acknowledged regarding:
- Need for additional testing across different conditions
- Model robustness requirements
- Potential need for training on different amputation types
4. The scale and scope of testing (22 scans, 6312 images) provides sufficient preliminary evidence for the hypothesis

The claim represents a well-calibrated statement that aligns with the strength of the supporting evidence without overextending beyond what the data can support.


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

We hypothesize that automating image segmentation via DeepLabv3 then rendering the segmented images using photogrammetry could create an efficient processing pipeline for scanning amputated limbs with smartphones.

The review comment to be evaluated is:

**Strengths**
- The paper is easy to follow and contains nice visualisation
- The pipeline is evaluated not only for the segmentation quality but also its downstream task: 3D rendering
- There are some interesting qualitative descriptions of the results

**Weaknesses**
- There is little to no technical novelty, all employed methods have been previously presented and are simply applied to a new task
- The description of the dataset and train/validation split is slightly confusing: it is stated that the "fine- tuning dataset consisted of 806 manually labeled images" and further "we randomly select 80% of the images as our training data set and reserve the remaining 20% for validation" does this mean the same subject can be part of training and validation?
- Later on the authors state: "Of the 24 limb scans acquired in the previous step, two scans were manually segmented via Adobe Photoshop and set aside as ground truth references. The remaining 22 scans, totaling 6312 images, were fed into the fine-tuned neural network for segmentation.". Are those different subjects to the ones used for fine-tuning? Furthermore, since 24 scans correspond to 12 subjects with or without additional sock, again the question of whether the 22/2 split is done on a subject-level?
- Overall the low number (2) of held-out ground truth cases makes it somewhat  hard to trust those results, since some cherry-picking might be unavoidable
- All models converge very quickly and no data augmentation seems to be used, this could indicate some overfitting
- an additional practical problem of the pipeline seems to be failure of image alignment that caused scans 2, 7, 8 and 14 to not render properly with the automated workflow. I wonder whether the authors have attempted to use the automatic segmentation information or some other features from the fine-tuned network to improve on this?
- the inference times for automatic segmentation seem awfully slow (0.32 images/s), even when being restricted to Google Colab infrastructure measuring the feed-forward path of a DeepLabV3 with mixed precision (AMP) on a Tesla T4 should result in at least two orders of magnitude faster throughput

The evidence to be evaluated is:In this context, photogrammetry has been used extensively since the 1950's for medical imaging and measurementNewton & Mitchell (1996). The development of digital cameras and the accompanying transition to digital photogrammetry has led to technologies for the reconstruction of 3D models using photogrammetric algorithms Linder (2016). Digital photogrammetry has been used successfully to reconstruct patient anatomy in many medical contexts such as cranial deformation scanning Barbero-Garcia et al. (2017, 2020, 2021), facial scanning Ross et al. (2018); Nightingale et al. (2020, 2021), and amputated limb scanning R. B. Taqriban et al. (2019); Cabrera et al. (2020); Ismail et al. (2020); De Vivo Nicoloso et al. (2021).

Close range photogrammetric approaches have been proven to have accuracy comparable to clinical gold standard technologies Ross et al. (2018). Using an Artec Spider structured light scanner as a clinical "gold standard" reference, Nightingale et al. (2020) found that photogrammetric reconstructions of facial scans using 80 images captured had RMSE accuracy values of 1.3 mm \(\pm\) 0.3 mm. In a similar study, Nightingale et al. (2021) achieved RMSE accuracy values of 1.5 mm \(\pm\) 0.4 mm with 30 photographs on reconstructions of the external ear.

For this reason, recent research has focused on various methods for automating photogrammetric workflows for anatomical scanning Barbero-Garcia et al. (2020); Cabrera et al. (2020).

Automated approaches have focused their attention on this acquisition stage to ensure completeness of the results Nocerino et al. (2017) with recent advances incorporating machine learning for landmark detection Barbero-Garcia et al. (2021). Still, automation of photogrammetric image processing (specifically image segmentation) remains a large problem Cabrera et al. (2021). Automating this image segmentation step could dramatically increase the speed of photogrammetric workflows for medical imaging, improving the clinical viability. Medical image segmentation plays an essential role in modern clinical practice by facilitating computer aided diagnoses and making patient anatomical structures clear Wang et al. (2022). Prior to recent developments in deep learning (DL), computer vision techniques such as k-nearest neighbors (kNN), decision trees (DT), support vector machines (SVM), and random forest (RF) models were utilized for segmentation and classification tasks Thanh Noi and Kappas (2017)

DL has superseded all of these approaches in medical imaging for several reasons, but most notably because the burden of feature engineering in DL shifts from humans to computers Shen et al. (2017). CNNs are the most heavily researched DL architectures for medical image analysis J. Ker et al. (2018).

The DeepLabv3 architecture expands on traditional CNNs by utilizing atrous convolution with spatial pyramid pooling Chen et al. (2017). Atrous convolution utilizes a dilated convolution kernel that, rather than focusing on adjacent pixels, convolves pixels that are not immediate neighbors. Atrous convolution helps to resolve the issue of spatial resolution loss in feature maps from successive convolution and de-convolution steps by allowing for precise control of feature map density. Atrous Spatial Pyramid Pooling (ASPP) Chen et al. (2017) utilizes four parallel atrous convolutions with different atrous rates to transform feature maps. This allows for accurate and efficient classification regions at arbitrary scales Chen et al. (2017). These elements of the DeepLabv3 architecture result in better classification of pixels for semantic image segmentation, on par with other state-of-the-art models.

Recent research has shown great success in using transfer learning with the DeepLabv3 architecture for medical imaging segmentation Roy et al. (2020). We performed transfer learning on a pre-trained resnet-101 based DeepLabv3 model downloaded from the Pytorch Model Zoo Paszke et al. (2019). This model was pre-trained on the COCO train2017 data set comprising 200,000 images and 80 object categories Lin et al. (2015). Our fine-tuning dataset consisted of 806 manually labeled images of limbs with transtibial amputations.

Following this, we randomly select 80% of the images as our training data set and reserve the remaining 20% for validation.

We captured 24 limb scans to test the applicability of the neural network model for image segmentation. 12 scans were performed with a single transtibial amputee wearing a standard liner then 12 additional scans were performed with the transtibial amputee wearing a colored sock over their standard liner. Scans were performed utilizing the Lim(b)itless smartphone application Cabrera et al. (2020) on a Motorola Moto G7 (MSRP $299, Camera - 12 MP, f/1.8, 1/2.8") see Fig. 1. The scanning procedure involved making two revolutions (one clockwise, one counterclockwise) around the outside of the limb while keeping the limb in frame of the application. Scans were taken at a constant radius of 30 cm from the limb, similar to Barbero-Garcia et al. (2018), at a rotation rate of two revolutions per minute. This application automatically initiates a capture sequence at 10 degree intervals with each capture sequence including a burst of 3 photographs.

The remaining 22 scans, totaling 6312 images, were fed into the fine-tuned neural network for segmentation. Segmentation time using the neural network averaged 890 s \(\pm\) 111 s, Table 1. 3D limb models were rendered from these segmented images using Agisoft Metashape, Fig. 2.

Finally, STL meshes of the scans rendered from automatically segmented images were compared with scans rendered from ground truth images via the CloudCompare open source 3D point cloud and mesh processing software.

After 10 epochs of training, the fine tuned neural network showed excellent performance in segmenting images of transtibial amputations, see Fig. 4.

However, the testing IoU remains largely constant, with a value of 79.9% after 10 epochs of training. This IoU is comparable with values reported by Chen et al. (2017b) which reached peak mIoU values of 81.3% on the Cityscapes test set and 85.7% on the PASCAL VOC 2012 test set with the DeepLabv3 architecture.

Increasing the number of images in the training dataset is feasible, but would require scanning additional persons with limb loss and manual segmentation. As with other forms of medical imaging, limited data quantities and expert annotations present hurdles for deep learning techniques Wang et al. (2018). On the other hand, reducing the image downsampling could improve the model performance without requiring additional annotations. In Fig. 4 it can be observed that the masks generated by the neural network are partially limited in accuracy due to this downsampling.

This is important because poor surface finish can be directly tied to the quality of image segmentation, with better segmentation leading to smoother finishes.

Unlike the skulls studied by Barbero-Garcia et al. (2017, 2020), amputated limbs undergo constant shape and deformation changes due to effects of donning-doffing, muscle contractions, interstitial fluid movements etc.

The advances in photogrammetric limb scanning resulting from automation of the segmentation step could have potentially significant consequences. Notably, automatic segmentation decreases the amount of human time and effort required to render photogrammetric models. As mentioned previously in the methods, a limb scan captured via the Lim(b)ittless smartphone application captures at least 216 images. Manual segmentation of those images would take (at minimum) 1 minute per image leading to a segmentation time of 12,960 seconds. The fine-tuned network trained in this paper would only take 675 seconds to perform this task based on the segmentation rate reported in Table 1.

Evaluating the end to end process using the rate values in Table 1 reveals that the model rendering workflow is 4.88x faster overall.

Models from the updated photogrammetric workflow could be used in a variety of contexts, since there was no significant geometrical difference between ground truth models and models rendered from images segmented by the fine-tuned neural network. Most notably, photogrammetric models built from smartphone scans can facilitate a host of low-cost telemedicine solutions. As one example, the limb models could be utilized as the first stage of digital prosthetic socket rectification. This would enable patients to be able to be scanned outside of a clinical environment (eg. at home) and have a prosthesis fabricated digitally and shipped to them. Aside from this, one distinct advantage of the photogrammetric process is it retains surface features which can be reprojected onto the final 3D model. With UV texture mapping, doctors could get a 3D view of limbs and look closely at their outer surfaces (such as for lesions) in virtual reality (VR).

Being able to capture data outside of a clinical environment could help clinicians to increase the number of data points and the frequency of measurements beyond what is currently possible.

On a technical level, the fine-tuned segmentation model from this research would need to be evaluated for robustness. Obtaining more scans of different skin colors, different lighting environments, etc. while training the model further could improve the robustness and reduce the risk of bias. It remains to be seen whether this model could be applied to segment different types of amputation or if the model would need to be trained on additional examples.

This research sought to address limitations in photogrammetric workflows by automating image segmentation via a fine-tuned deep learning model.

Applying this fine-tuned model to segment a dataset of 22 scans containing 6312 images showed that there was no significant geometric difference compared to scans rendered from manually segmented images. The MAE was found to be on average 0.57 mm \(\pm\) 0.63 mm while the RMSE was found to be on average 2.43 mm \(\pm\) 1.10 mm. The error reported was mostly influenced by slight changes in limb shape between scans and not render noise caused by image segmentation.

Using the fine-tuned model increased the rate of image segmentation by 19.2x as well as sped up the entire photogrammetric workflow by a factor of 4.88x.

This fine-tuned deep learning model can help facilitate large scale image processing for telemedicine applications. Using smartphones to acquire 3D scans of amputated limbs, clinicians could monitor patients, conduct large scale research studies, and even build prostheses remotely. This could potentially increase the standard of care for persons with limb loss who do not live near a clinic by matching patients with clinicians seeking to expand their geographic service area. \begin{table}
\begin{tabular}{l l} \hline
**Number of Images** & 287 \(\pm\) 29 \\
**Scan Time (s)** & 52.4 \(\pm\) 6.1 \\
**Segmentation Time (s)** & 890 \(\pm\) 111 \\
**Render Time (s)** & 3430 \(\pm\) 850 \\
**Scan Rate (images/s)** & 5.54 \(\pm\) 0.74 \\
**Segmentation Rate (images/s)** & 0.32 \(\pm\) 0.01 \\
**Render Rate (images/s)** & 0.088 \(\pm\) 0.022 \\ \hline \end{tabular}
\end{table}
Table 1: Image acquisition and rendering pipeline statistics for smartphone scans (n = 22) used in this study.

Input: Table 1: Image acquisition and rendering pipeline statistics for smartphone scans (n = 22) used in this study.

Input: Figure 4: Limbs with transtibial amputations used in the transfer learning process. A) These limb photographs were taken in a variety of lighting conditions and with different liners. B) Manually segmented binary masks representing the ground truth. C) Masks generated by the fine-tuned neural network. D) Applying the masks from the fine-tuned neural network to the original images.

Input: Figure 5: Selected comparison of limbs in CloudCompare. In these 3D scans, it is evident that the large RMSE is due to a change in geometry from limb flexure, and is not due to segmentation or render quality. A) Ground truth model rendered from manually segmented images. B) Scan 12 - Highest RMSE (5.54 mm) evident limb flexure C) Scan 13 - Low RMSE (1.26 mm) no limb flexure D) Scan 16 - Medium RMSE (3.31 mm) evident limb flexure E) Scan 17 - Medium RMSE (2.71 mm) evident surface noise and limb flexure.

Input: Figure 1: Scanning a Transtibial Amputee using the Lim(b)itless Cell Phone Application

Input: Figure 3: Evolution of IoU and loss function for training and testing dataset

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim and evidence, I find this to be a relatively well-stated hypothesis for several reasons:

- The evidence demonstrates successful implementation of DeepLabv3 for limb segmentation with 79.9% IoU, comparable to benchmark results
- The automated pipeline achieved 19.2x faster segmentation and 4.88x faster overall workflow compared to manual methods
- The geometric accuracy was maintained (MAE 0.57mm ±0.63mm)
- The authors acknowledge limitations and needs for further validation (e.g., different skin colors, lighting conditions, amputation types)
- The reviewer's concerns about technical novelty and small validation set are valid but don't invalidate the basic hypothesis about creating an efficient pipeline

The claim is appropriately framed as a hypothesis rather than a definitive conclusion.

<score>2</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

We hypothesize that automating image segmentation via DeepLabv3 then rendering the segmented images using photogrammetry could create an efficient processing pipeline for scanning amputated limbs with smartphones.

The review comment to be evaluated is:

This paper proposes a method using deep learning with photogrammetry for scanning amputated limbs. The proposed method uses deep convolutional neural networks, DeepLabv3, and transfer learning. The ResNet-101-based Deep Labv3 model was pre-trained and then fine-tuned. The scan images were acquired by using a smartphone and subsequently segmented on a desktop. Afterward, 3D limb models were rendered from the segmented images using Agisoft Metashape.

The evidence to be evaluated is:In this context, photogrammetry has been used extensively since the 1950's for medical imaging and measurementNewton & Mitchell (1996). The development of digital cameras and the accompanying transition to digital photogrammetry has led to technologies for the reconstruction of 3D models using photogrammetric algorithms Linder (2016). Digital photogrammetry has been used successfully to reconstruct patient anatomy in many medical contexts such as cranial deformation scanning Barbero-Garcia et al. (2017, 2020, 2021), facial scanning Ross et al. (2018); Nightingale et al. (2020, 2021), and amputated limb scanning R. B. Taqriban et al. (2019); Cabrera et al. (2020); Ismail et al. (2020); De Vivo Nicoloso et al. (2021).

Close range photogrammetric approaches have been proven to have accuracy comparable to clinical gold standard technologies Ross et al. (2018). Using an Artec Spider structured light scanner as a clinical "gold standard" reference, Nightingale et al. (2020) found that photogrammetric reconstructions of facial scans using 80 images captured had RMSE accuracy values of 1.3 mm \(\pm\) 0.3 mm. In a similar study, Nightingale et al. (2021) achieved RMSE accuracy values of 1.5 mm \(\pm\) 0.4 mm with 30 photographs on reconstructions of the external ear.

For this reason, recent research has focused on various methods for automating photogrammetric workflows for anatomical scanning Barbero-Garcia et al. (2020); Cabrera et al. (2020).

Automated approaches have focused their attention on this acquisition stage to ensure completeness of the results Nocerino et al. (2017) with recent advances incorporating machine learning for landmark detection Barbero-Garcia et al. (2021). Still, automation of photogrammetric image processing (specifically image segmentation) remains a large problem Cabrera et al. (2021). Automating this image segmentation step could dramatically increase the speed of photogrammetric workflows for medical imaging, improving the clinical viability. Medical image segmentation plays an essential role in modern clinical practice by facilitating computer aided diagnoses and making patient anatomical structures clear Wang et al. (2022). Prior to recent developments in deep learning (DL), computer vision techniques such as k-nearest neighbors (kNN), decision trees (DT), support vector machines (SVM), and random forest (RF) models were utilized for segmentation and classification tasks Thanh Noi and Kappas (2017)

DL has superseded all of these approaches in medical imaging for several reasons, but most notably because the burden of feature engineering in DL shifts from humans to computers Shen et al. (2017). CNNs are the most heavily researched DL architectures for medical image analysis J. Ker et al. (2018).

The DeepLabv3 architecture expands on traditional CNNs by utilizing atrous convolution with spatial pyramid pooling Chen et al. (2017). Atrous convolution utilizes a dilated convolution kernel that, rather than focusing on adjacent pixels, convolves pixels that are not immediate neighbors. Atrous convolution helps to resolve the issue of spatial resolution loss in feature maps from successive convolution and de-convolution steps by allowing for precise control of feature map density. Atrous Spatial Pyramid Pooling (ASPP) Chen et al. (2017) utilizes four parallel atrous convolutions with different atrous rates to transform feature maps. This allows for accurate and efficient classification regions at arbitrary scales Chen et al. (2017). These elements of the DeepLabv3 architecture result in better classification of pixels for semantic image segmentation, on par with other state-of-the-art models.

Recent research has shown great success in using transfer learning with the DeepLabv3 architecture for medical imaging segmentation Roy et al. (2020). We performed transfer learning on a pre-trained resnet-101 based DeepLabv3 model downloaded from the Pytorch Model Zoo Paszke et al. (2019). This model was pre-trained on the COCO train2017 data set comprising 200,000 images and 80 object categories Lin et al. (2015). Our fine-tuning dataset consisted of 806 manually labeled images of limbs with transtibial amputations.

Following this, we randomly select 80% of the images as our training data set and reserve the remaining 20% for validation.

We captured 24 limb scans to test the applicability of the neural network model for image segmentation. 12 scans were performed with a single transtibial amputee wearing a standard liner then 12 additional scans were performed with the transtibial amputee wearing a colored sock over their standard liner. Scans were performed utilizing the Lim(b)itless smartphone application Cabrera et al. (2020) on a Motorola Moto G7 (MSRP $299, Camera - 12 MP, f/1.8, 1/2.8") see Fig. 1. The scanning procedure involved making two revolutions (one clockwise, one counterclockwise) around the outside of the limb while keeping the limb in frame of the application. Scans were taken at a constant radius of 30 cm from the limb, similar to Barbero-Garcia et al. (2018), at a rotation rate of two revolutions per minute. This application automatically initiates a capture sequence at 10 degree intervals with each capture sequence including a burst of 3 photographs.

The remaining 22 scans, totaling 6312 images, were fed into the fine-tuned neural network for segmentation. Segmentation time using the neural network averaged 890 s \(\pm\) 111 s, Table 1. 3D limb models were rendered from these segmented images using Agisoft Metashape, Fig. 2.

Finally, STL meshes of the scans rendered from automatically segmented images were compared with scans rendered from ground truth images via the CloudCompare open source 3D point cloud and mesh processing software.

After 10 epochs of training, the fine tuned neural network showed excellent performance in segmenting images of transtibial amputations, see Fig. 4.

However, the testing IoU remains largely constant, with a value of 79.9% after 10 epochs of training. This IoU is comparable with values reported by Chen et al. (2017b) which reached peak mIoU values of 81.3% on the Cityscapes test set and 85.7% on the PASCAL VOC 2012 test set with the DeepLabv3 architecture.

Increasing the number of images in the training dataset is feasible, but would require scanning additional persons with limb loss and manual segmentation. As with other forms of medical imaging, limited data quantities and expert annotations present hurdles for deep learning techniques Wang et al. (2018). On the other hand, reducing the image downsampling could improve the model performance without requiring additional annotations. In Fig. 4 it can be observed that the masks generated by the neural network are partially limited in accuracy due to this downsampling.

This is important because poor surface finish can be directly tied to the quality of image segmentation, with better segmentation leading to smoother finishes.

Unlike the skulls studied by Barbero-Garcia et al. (2017, 2020), amputated limbs undergo constant shape and deformation changes due to effects of donning-doffing, muscle contractions, interstitial fluid movements etc.

The advances in photogrammetric limb scanning resulting from automation of the segmentation step could have potentially significant consequences. Notably, automatic segmentation decreases the amount of human time and effort required to render photogrammetric models. As mentioned previously in the methods, a limb scan captured via the Lim(b)ittless smartphone application captures at least 216 images. Manual segmentation of those images would take (at minimum) 1 minute per image leading to a segmentation time of 12,960 seconds. The fine-tuned network trained in this paper would only take 675 seconds to perform this task based on the segmentation rate reported in Table 1.

Evaluating the end to end process using the rate values in Table 1 reveals that the model rendering workflow is 4.88x faster overall.

Models from the updated photogrammetric workflow could be used in a variety of contexts, since there was no significant geometrical difference between ground truth models and models rendered from images segmented by the fine-tuned neural network. Most notably, photogrammetric models built from smartphone scans can facilitate a host of low-cost telemedicine solutions. As one example, the limb models could be utilized as the first stage of digital prosthetic socket rectification. This would enable patients to be able to be scanned outside of a clinical environment (eg. at home) and have a prosthesis fabricated digitally and shipped to them. Aside from this, one distinct advantage of the photogrammetric process is it retains surface features which can be reprojected onto the final 3D model. With UV texture mapping, doctors could get a 3D view of limbs and look closely at their outer surfaces (such as for lesions) in virtual reality (VR).

Being able to capture data outside of a clinical environment could help clinicians to increase the number of data points and the frequency of measurements beyond what is currently possible.

On a technical level, the fine-tuned segmentation model from this research would need to be evaluated for robustness. Obtaining more scans of different skin colors, different lighting environments, etc. while training the model further could improve the robustness and reduce the risk of bias. It remains to be seen whether this model could be applied to segment different types of amputation or if the model would need to be trained on additional examples.

This research sought to address limitations in photogrammetric workflows by automating image segmentation via a fine-tuned deep learning model.

Applying this fine-tuned model to segment a dataset of 22 scans containing 6312 images showed that there was no significant geometric difference compared to scans rendered from manually segmented images. The MAE was found to be on average 0.57 mm \(\pm\) 0.63 mm while the RMSE was found to be on average 2.43 mm \(\pm\) 1.10 mm. The error reported was mostly influenced by slight changes in limb shape between scans and not render noise caused by image segmentation.

Using the fine-tuned model increased the rate of image segmentation by 19.2x as well as sped up the entire photogrammetric workflow by a factor of 4.88x.

This fine-tuned deep learning model can help facilitate large scale image processing for telemedicine applications. Using smartphones to acquire 3D scans of amputated limbs, clinicians could monitor patients, conduct large scale research studies, and even build prostheses remotely. This could potentially increase the standard of care for persons with limb loss who do not live near a clinic by matching patients with clinicians seeking to expand their geographic service area. \begin{table}
\begin{tabular}{l l} \hline
**Number of Images** & 287 \(\pm\) 29 \\
**Scan Time (s)** & 52.4 \(\pm\) 6.1 \\
**Segmentation Time (s)** & 890 \(\pm\) 111 \\
**Render Time (s)** & 3430 \(\pm\) 850 \\
**Scan Rate (images/s)** & 5.54 \(\pm\) 0.74 \\
**Segmentation Rate (images/s)** & 0.32 \(\pm\) 0.01 \\
**Render Rate (images/s)** & 0.088 \(\pm\) 0.022 \\ \hline \end{tabular}
\end{table}
Table 1: Image acquisition and rendering pipeline statistics for smartphone scans (n = 22) used in this study.

Input: Table 1: Image acquisition and rendering pipeline statistics for smartphone scans (n = 22) used in this study.

Input: Figure 4: Limbs with transtibial amputations used in the transfer learning process. A) These limb photographs were taken in a variety of lighting conditions and with different liners. B) Manually segmented binary masks representing the ground truth. C) Masks generated by the fine-tuned neural network. D) Applying the masks from the fine-tuned neural network to the original images.

Input: Figure 5: Selected comparison of limbs in CloudCompare. In these 3D scans, it is evident that the large RMSE is due to a change in geometry from limb flexure, and is not due to segmentation or render quality. A) Ground truth model rendered from manually segmented images. B) Scan 12 - Highest RMSE (5.54 mm) evident limb flexure C) Scan 13 - Low RMSE (1.26 mm) no limb flexure D) Scan 16 - Medium RMSE (3.31 mm) evident limb flexure E) Scan 17 - Medium RMSE (2.71 mm) evident surface noise and limb flexure.

Input: Figure 1: Scanning a Transtibial Amputee using the Lim(b)itless Cell Phone Application

Input: Figure 3: Evolution of IoU and loss function for training and testing dataset

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the evidence, the claim about automating image segmentation via DeepLabv3 and photogrammetry appears well-stated and supported by the presented data. The evidence shows:
- Successful implementation with IoU of 79.9%, comparable to benchmark results
- 19.2x faster segmentation and 4.88x faster overall workflow
- Acceptable accuracy with MAE of 0.57mm ±0.63mm and RMSE of 2.43mm ±1.10mm
- Clear validation through testing on 22 scans (6312 images)
- Appropriate acknowledgment of limitations and future work needed

The hypothesis is conservatively stated as a potential approach rather than making definitive claims, and the evidence strongly supports its feasibility.

<score>1</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

We hypothesize that automating image segmentation via DeepLabv3 then rendering the segmented images using photogrammetry could create an efficient processing pipeline for scanning amputated limbs with smartphones.

The review comment to be evaluated is:

The problem statement is clearly explained and the method addresses an important problem of improving time and accuracy of healthcare procedures. A very detailed discussion of clinical impact and directions is presented at the end of the work, what helps with understanding potential applications, advantages and limitations of the method. A few ideas for improvements are also presented, showing that authors have a good understanding of the presented medical problem.

Unfortunately, the novelty of the work is limited. Very similar studies using deep learning segmentation topologies for improvements of photogrammetry have already been conducted. The work is an application of a known method to another computer vision dataset, but experiments focus only on a single model, so it may be difficult to draw some conclusions for future enhancements of such methods.

Also, the reason for selection of the specific DeepLab model hasn't been justified. It would be interesting to verify other models as well.

It's also mentioned that photo alignment led to some failure cases. There are various alignment techniques that could be used to improve this outcome. Have you tried any of them?

The evidence to be evaluated is:In this context, photogrammetry has been used extensively since the 1950's for medical imaging and measurementNewton & Mitchell (1996). The development of digital cameras and the accompanying transition to digital photogrammetry has led to technologies for the reconstruction of 3D models using photogrammetric algorithms Linder (2016). Digital photogrammetry has been used successfully to reconstruct patient anatomy in many medical contexts such as cranial deformation scanning Barbero-Garcia et al. (2017, 2020, 2021), facial scanning Ross et al. (2018); Nightingale et al. (2020, 2021), and amputated limb scanning R. B. Taqriban et al. (2019); Cabrera et al. (2020); Ismail et al. (2020); De Vivo Nicoloso et al. (2021).

Close range photogrammetric approaches have been proven to have accuracy comparable to clinical gold standard technologies Ross et al. (2018). Using an Artec Spider structured light scanner as a clinical "gold standard" reference, Nightingale et al. (2020) found that photogrammetric reconstructions of facial scans using 80 images captured had RMSE accuracy values of 1.3 mm \(\pm\) 0.3 mm. In a similar study, Nightingale et al. (2021) achieved RMSE accuracy values of 1.5 mm \(\pm\) 0.4 mm with 30 photographs on reconstructions of the external ear.

For this reason, recent research has focused on various methods for automating photogrammetric workflows for anatomical scanning Barbero-Garcia et al. (2020); Cabrera et al. (2020).

Automated approaches have focused their attention on this acquisition stage to ensure completeness of the results Nocerino et al. (2017) with recent advances incorporating machine learning for landmark detection Barbero-Garcia et al. (2021). Still, automation of photogrammetric image processing (specifically image segmentation) remains a large problem Cabrera et al. (2021). Automating this image segmentation step could dramatically increase the speed of photogrammetric workflows for medical imaging, improving the clinical viability. Medical image segmentation plays an essential role in modern clinical practice by facilitating computer aided diagnoses and making patient anatomical structures clear Wang et al. (2022). Prior to recent developments in deep learning (DL), computer vision techniques such as k-nearest neighbors (kNN), decision trees (DT), support vector machines (SVM), and random forest (RF) models were utilized for segmentation and classification tasks Thanh Noi and Kappas (2017)

DL has superseded all of these approaches in medical imaging for several reasons, but most notably because the burden of feature engineering in DL shifts from humans to computers Shen et al. (2017). CNNs are the most heavily researched DL architectures for medical image analysis J. Ker et al. (2018).

The DeepLabv3 architecture expands on traditional CNNs by utilizing atrous convolution with spatial pyramid pooling Chen et al. (2017). Atrous convolution utilizes a dilated convolution kernel that, rather than focusing on adjacent pixels, convolves pixels that are not immediate neighbors. Atrous convolution helps to resolve the issue of spatial resolution loss in feature maps from successive convolution and de-convolution steps by allowing for precise control of feature map density. Atrous Spatial Pyramid Pooling (ASPP) Chen et al. (2017) utilizes four parallel atrous convolutions with different atrous rates to transform feature maps. This allows for accurate and efficient classification regions at arbitrary scales Chen et al. (2017). These elements of the DeepLabv3 architecture result in better classification of pixels for semantic image segmentation, on par with other state-of-the-art models.

Recent research has shown great success in using transfer learning with the DeepLabv3 architecture for medical imaging segmentation Roy et al. (2020). We performed transfer learning on a pre-trained resnet-101 based DeepLabv3 model downloaded from the Pytorch Model Zoo Paszke et al. (2019). This model was pre-trained on the COCO train2017 data set comprising 200,000 images and 80 object categories Lin et al. (2015). Our fine-tuning dataset consisted of 806 manually labeled images of limbs with transtibial amputations.

Following this, we randomly select 80% of the images as our training data set and reserve the remaining 20% for validation.

We captured 24 limb scans to test the applicability of the neural network model for image segmentation. 12 scans were performed with a single transtibial amputee wearing a standard liner then 12 additional scans were performed with the transtibial amputee wearing a colored sock over their standard liner. Scans were performed utilizing the Lim(b)itless smartphone application Cabrera et al. (2020) on a Motorola Moto G7 (MSRP $299, Camera - 12 MP, f/1.8, 1/2.8") see Fig. 1. The scanning procedure involved making two revolutions (one clockwise, one counterclockwise) around the outside of the limb while keeping the limb in frame of the application. Scans were taken at a constant radius of 30 cm from the limb, similar to Barbero-Garcia et al. (2018), at a rotation rate of two revolutions per minute. This application automatically initiates a capture sequence at 10 degree intervals with each capture sequence including a burst of 3 photographs.

The remaining 22 scans, totaling 6312 images, were fed into the fine-tuned neural network for segmentation. Segmentation time using the neural network averaged 890 s \(\pm\) 111 s, Table 1. 3D limb models were rendered from these segmented images using Agisoft Metashape, Fig. 2.

Finally, STL meshes of the scans rendered from automatically segmented images were compared with scans rendered from ground truth images via the CloudCompare open source 3D point cloud and mesh processing software.

After 10 epochs of training, the fine tuned neural network showed excellent performance in segmenting images of transtibial amputations, see Fig. 4.

However, the testing IoU remains largely constant, with a value of 79.9% after 10 epochs of training. This IoU is comparable with values reported by Chen et al. (2017b) which reached peak mIoU values of 81.3% on the Cityscapes test set and 85.7% on the PASCAL VOC 2012 test set with the DeepLabv3 architecture.

Increasing the number of images in the training dataset is feasible, but would require scanning additional persons with limb loss and manual segmentation. As with other forms of medical imaging, limited data quantities and expert annotations present hurdles for deep learning techniques Wang et al. (2018). On the other hand, reducing the image downsampling could improve the model performance without requiring additional annotations. In Fig. 4 it can be observed that the masks generated by the neural network are partially limited in accuracy due to this downsampling.

This is important because poor surface finish can be directly tied to the quality of image segmentation, with better segmentation leading to smoother finishes.

Unlike the skulls studied by Barbero-Garcia et al. (2017, 2020), amputated limbs undergo constant shape and deformation changes due to effects of donning-doffing, muscle contractions, interstitial fluid movements etc.

The advances in photogrammetric limb scanning resulting from automation of the segmentation step could have potentially significant consequences. Notably, automatic segmentation decreases the amount of human time and effort required to render photogrammetric models. As mentioned previously in the methods, a limb scan captured via the Lim(b)ittless smartphone application captures at least 216 images. Manual segmentation of those images would take (at minimum) 1 minute per image leading to a segmentation time of 12,960 seconds. The fine-tuned network trained in this paper would only take 675 seconds to perform this task based on the segmentation rate reported in Table 1.

Evaluating the end to end process using the rate values in Table 1 reveals that the model rendering workflow is 4.88x faster overall.

Models from the updated photogrammetric workflow could be used in a variety of contexts, since there was no significant geometrical difference between ground truth models and models rendered from images segmented by the fine-tuned neural network. Most notably, photogrammetric models built from smartphone scans can facilitate a host of low-cost telemedicine solutions. As one example, the limb models could be utilized as the first stage of digital prosthetic socket rectification. This would enable patients to be able to be scanned outside of a clinical environment (eg. at home) and have a prosthesis fabricated digitally and shipped to them. Aside from this, one distinct advantage of the photogrammetric process is it retains surface features which can be reprojected onto the final 3D model. With UV texture mapping, doctors could get a 3D view of limbs and look closely at their outer surfaces (such as for lesions) in virtual reality (VR).

Being able to capture data outside of a clinical environment could help clinicians to increase the number of data points and the frequency of measurements beyond what is currently possible.

On a technical level, the fine-tuned segmentation model from this research would need to be evaluated for robustness. Obtaining more scans of different skin colors, different lighting environments, etc. while training the model further could improve the robustness and reduce the risk of bias. It remains to be seen whether this model could be applied to segment different types of amputation or if the model would need to be trained on additional examples.

This research sought to address limitations in photogrammetric workflows by automating image segmentation via a fine-tuned deep learning model.

Applying this fine-tuned model to segment a dataset of 22 scans containing 6312 images showed that there was no significant geometric difference compared to scans rendered from manually segmented images. The MAE was found to be on average 0.57 mm \(\pm\) 0.63 mm while the RMSE was found to be on average 2.43 mm \(\pm\) 1.10 mm. The error reported was mostly influenced by slight changes in limb shape between scans and not render noise caused by image segmentation.

Using the fine-tuned model increased the rate of image segmentation by 19.2x as well as sped up the entire photogrammetric workflow by a factor of 4.88x.

This fine-tuned deep learning model can help facilitate large scale image processing for telemedicine applications. Using smartphones to acquire 3D scans of amputated limbs, clinicians could monitor patients, conduct large scale research studies, and even build prostheses remotely. This could potentially increase the standard of care for persons with limb loss who do not live near a clinic by matching patients with clinicians seeking to expand their geographic service area. \begin{table}
\begin{tabular}{l l} \hline
**Number of Images** & 287 \(\pm\) 29 \\
**Scan Time (s)** & 52.4 \(\pm\) 6.1 \\
**Segmentation Time (s)** & 890 \(\pm\) 111 \\
**Render Time (s)** & 3430 \(\pm\) 850 \\
**Scan Rate (images/s)** & 5.54 \(\pm\) 0.74 \\
**Segmentation Rate (images/s)** & 0.32 \(\pm\) 0.01 \\
**Render Rate (images/s)** & 0.088 \(\pm\) 0.022 \\ \hline \end{tabular}
\end{table}
Table 1: Image acquisition and rendering pipeline statistics for smartphone scans (n = 22) used in this study.

Input: Table 1: Image acquisition and rendering pipeline statistics for smartphone scans (n = 22) used in this study.

Input: Figure 4: Limbs with transtibial amputations used in the transfer learning process. A) These limb photographs were taken in a variety of lighting conditions and with different liners. B) Manually segmented binary masks representing the ground truth. C) Masks generated by the fine-tuned neural network. D) Applying the masks from the fine-tuned neural network to the original images.

Input: Figure 5: Selected comparison of limbs in CloudCompare. In these 3D scans, it is evident that the large RMSE is due to a change in geometry from limb flexure, and is not due to segmentation or render quality. A) Ground truth model rendered from manually segmented images. B) Scan 12 - Highest RMSE (5.54 mm) evident limb flexure C) Scan 13 - Low RMSE (1.26 mm) no limb flexure D) Scan 16 - Medium RMSE (3.31 mm) evident limb flexure E) Scan 17 - Medium RMSE (2.71 mm) evident surface noise and limb flexure.

Input: Figure 1: Scanning a Transtibial Amputee using the Lim(b)itless Cell Phone Application

Input: Figure 3: Evolution of IoU and loss function for training and testing dataset

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
1. The claim appears well-stated and appropriately cautious based on the evidence presented. The research demonstrates:
- Successful implementation of DeepLabv3 for limb segmentation with 79.9% IoU
- 19.2x faster segmentation and 4.88x faster overall workflow
- Comparable geometric accuracy (MAE 0.57mm ±0.63mm) to manual segmentation
- Clear experimental validation with 22 scans and 6312 images
- Appropriate acknowledgment of limitations and need for further validation

The hypothesis is conservatively framed as a possibility ("could create") rather than making definitive claims, and is well-supported by the quantitative results.

<score>1</score>