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:

(2) Will segmenting 2D limb images using neural networks impact the accuracy of 3D models generated via photogrammetry?

The evidence to be evaluated is:We used Google Colab for training the model with dynamically scaled computer resources. In terms of the hyperparameters in fine-tuning, we used the Adam algorithm Kingma and Ba (2017) for optimizing the parameters (e.g. weights) in the neural network with the default learning rate being set to \(10^{-5}\). We employed the pixel-wise Mean Squared Error (MSE) as our loss function. We trained our fine-tuned neural network for 10 epochs using a batch size of 8 within each epoch. 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.

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. 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. All limbs were cropped to the same boundaries, and outliers (visualized as extraneous floating points) were filtered out consistent with the technique used by Ross et al. (2018).

This is important because poor surface finish can be directly tied to the quality of image segmentation, with better segmentation leading to smoother finishes. Although the accuracy is slightly diminished due to noise, it is not the main cause of error in these scans. Poor surface finish can be accounted for in practice by utilizing Laplacian smoothing functions.

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.

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.

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.

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.

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. 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.

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 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:
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:
I'll evaluate the claim "Will segmenting 2D limb images using neural networks impact the accuracy of 3D models generated via photogrammetry?"

Summary:
Based on the evidence provided, this claim is well-stated (Score 1). The research thoroughly demonstrates the impact through quantitative metrics:
- MAE of 0.57mm ± 0.63mm
- RMSE of 2.43mm ± 1.10mm
- IoU of 79.9% on testing
- Clear visualization in Figure 5 showing comparable results between manual and automated segmentation
The evidence shows that while there are slight differences, they are primarily due to limb flexure rather than segmentation quality. The research acknowledges limitations and provides comprehensive experimental details with appropriate statistical measures.

<score>1</score>

Justification:
1. The claim is supported by robust experimental design:
- 24 limb scans (12 with liner, 12 with colored sock)
- Controlled scanning conditions (30cm radius, consistent rotation)
- Ground truth comparisons
- Clear statistical analysis

2. Results are presented without exaggeration:
- Acknowledges sources of error (limb flexure vs. segmentation)
- Reports both positive outcomes and limitations
- Provides specific error measurements
- Shows visual evidence of comparison (Figure 5)

3. The research transparently discusses:
- Technical limitations
- Need for further validation
- Potential improvements
- Processing times and computational requirements

The evidence directly addresses the claim's focus on accuracy impact, making it a well-stated research question that aligns with the scope and findings of the study.


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:

(2) Will segmenting 2D limb images using neural networks impact the accuracy of 3D models generated via photogrammetry?

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:We used Google Colab for training the model with dynamically scaled computer resources. In terms of the hyperparameters in fine-tuning, we used the Adam algorithm Kingma and Ba (2017) for optimizing the parameters (e.g. weights) in the neural network with the default learning rate being set to \(10^{-5}\). We employed the pixel-wise Mean Squared Error (MSE) as our loss function. We trained our fine-tuned neural network for 10 epochs using a batch size of 8 within each epoch. 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.

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. 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. All limbs were cropped to the same boundaries, and outliers (visualized as extraneous floating points) were filtered out consistent with the technique used by Ross et al. (2018).

This is important because poor surface finish can be directly tied to the quality of image segmentation, with better segmentation leading to smoother finishes. Although the accuracy is slightly diminished due to noise, it is not the main cause of error in these scans. Poor surface finish can be accounted for in practice by utilizing Laplacian smoothing functions.

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.

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.

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.

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.

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. 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.

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 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:
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 neural networks' impact on 3D model accuracy appears to need careful evaluation. The evidence shows mixed results - while the mean absolute error (MAE) was relatively small at 0.57mm (±0.63mm), there are several important limitations:

1. The small validation set (only 2 ground truth scans)
2. Quick model convergence suggesting potential overfitting
3. Failed scans (2,7,8,14) due to alignment issues
4. Limited testing on only one subject with/without sock
5. The largest errors appear to come from limb flexure rather than segmentation quality

The evidence suggests neural network segmentation can work but requires more robust validation before making strong claims about accuracy impact.

<score>3</score>

Justification:
The claim requires a moderate score because:

1. The evidence shows the technique works (IoU of 79.9%, MAE of 0.57mm) but has clear limitations. As noted in the review comments, "the low number (2) of held-out ground truth cases makes it somewhat hard to trust those results."

2. Figure 5 demonstrates that geometric differences between scans are mainly due to limb flexure rather than segmentation quality, supported by the statement "The error reported was mostly influenced by slight changes in limb shape between scans and not render noise caused by image segmentation."

3. The reviewer points out potential overfitting issues: "All models converge very quickly and no data augmentation seems to be used, this could indicate some overfitting"

4. The dataset limitations (single subject, limited conditions) and failed scans suggest more validation is needed before making definitive claims about accuracy impact.

5. The paper acknowledges these limitations, stating "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."


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:

(2) Will segmenting 2D limb images using neural networks impact the accuracy of 3D models generated via photogrammetry?

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:We used Google Colab for training the model with dynamically scaled computer resources. In terms of the hyperparameters in fine-tuning, we used the Adam algorithm Kingma and Ba (2017) for optimizing the parameters (e.g. weights) in the neural network with the default learning rate being set to \(10^{-5}\). We employed the pixel-wise Mean Squared Error (MSE) as our loss function. We trained our fine-tuned neural network for 10 epochs using a batch size of 8 within each epoch. 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.

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. 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. All limbs were cropped to the same boundaries, and outliers (visualized as extraneous floating points) were filtered out consistent with the technique used by Ross et al. (2018).

This is important because poor surface finish can be directly tied to the quality of image segmentation, with better segmentation leading to smoother finishes. Although the accuracy is slightly diminished due to noise, it is not the main cause of error in these scans. Poor surface finish can be accounted for in practice by utilizing Laplacian smoothing functions.

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.

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.

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.

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.

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. 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.

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 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:
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:
I'll evaluate the claim "Will segmenting 2D limb images using neural networks impact the accuracy of 3D models generated via photogrammetry?"

Summary:
The evidence strongly supports that neural network segmentation has minimal impact on 3D model accuracy. The study shows no significant geometric differences between models from manually segmented (ground truth) and automatically segmented images, with MAE of 0.57mm (±0.63mm) and RMSE of 2.43mm (±1.10mm). Figure 5 demonstrates that larger deviations are primarily due to limb flexure rather than segmentation issues. The claim is well-stated as it simply questions the impact without overstating the results.

<score>1</score>

Justification:
1. The evidence directly addresses the claim with quantitative metrics comparing manual vs. automated segmentation
2. Figure 5 provides visual confirmation that differences are mainly due to limb position changes, not segmentation errors
3. The study acknowledges limitations and need for further validation across different conditions
4. The claim is phrased as a question rather than making definitive statements
5. Results show the automated segmentation approach maintains accuracy while reducing manual effort
6. The research demonstrates a practical IoU of 79.9% after fine-tuning

The claim is appropriately cautious and well-supported by the evidence presented.


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:

(2) Will segmenting 2D limb images using neural networks impact the accuracy of 3D models generated via photogrammetry?

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:We used Google Colab for training the model with dynamically scaled computer resources. In terms of the hyperparameters in fine-tuning, we used the Adam algorithm Kingma and Ba (2017) for optimizing the parameters (e.g. weights) in the neural network with the default learning rate being set to \(10^{-5}\). We employed the pixel-wise Mean Squared Error (MSE) as our loss function. We trained our fine-tuned neural network for 10 epochs using a batch size of 8 within each epoch. 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.

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. 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. All limbs were cropped to the same boundaries, and outliers (visualized as extraneous floating points) were filtered out consistent with the technique used by Ross et al. (2018).

This is important because poor surface finish can be directly tied to the quality of image segmentation, with better segmentation leading to smoother finishes. Although the accuracy is slightly diminished due to noise, it is not the main cause of error in these scans. Poor surface finish can be accounted for in practice by utilizing Laplacian smoothing functions.

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.

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.

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.

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.

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. 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.

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 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:
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 and review comments, I'll evaluate the claim "Will segmenting 2D limb images using neural networks impact the accuracy of 3D models generated via photogrammetry?"

Summary: The evidence strongly supports that neural network segmentation impacts 3D model accuracy, showing specific metrics (IoU of 79.9%, MAE of 0.57mm ±0.63mm, RMSE of 2.43mm ±1.10mm). The visual comparisons in Figure 5 and detailed analysis show that geometric differences between models are primarily due to limb flexure rather than segmentation issues. However, the reviewer correctly points out limitations - only one model type was tested, and alignment issues weren't fully explored. The claim is well-stated as it asks about impact without overstating the direction or magnitude of that impact.

<score>1</score>

Justification:
1. The evidence directly addresses the claim with quantitative metrics
2. The limitations are acknowledged in both the paper and review
3. The claim is phrased as a question about impact rather than making definitive statements
4. Visual and statistical evidence clearly shows the relationship between segmentation and model accuracy
5. The research includes appropriate controls (manual segmentation) for comparison


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:

After training, the model was able to successfully identify and segment limb images with an IoU of 79.9%.

The evidence to be evaluated is:The remaining 22 scans, totaling 6312 images, were fed into the fine-tuned neural network for segmentation.

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.

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. 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.

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:
I'll evaluate the claim "After training, the model was able to successfully identify and segment limb images with an IoU of 79.9%."

1. Summary Evaluation:
This claim is well-stated and accurately reflects the evidence without exaggeration. The evidence directly supports the IoU value of 79.9% through multiple mentions in the text and is visually confirmed in Figure 3, where the Test IoU line stabilizes around 80%. The claim doesn't overstate the performance, and the reported IoU is put into context by comparing it with similar studies (Chen et al.'s results of 81.3% and 85.7%). The claim is straightforward and factual without embellishment.

<score>1</score>

The claim receives a score of 1 (well-stated) because:
- The exact IoU value (79.9%) is directly supported by multiple instances in the evidence
- Figure 3 visually confirms the test IoU performance
- The claim doesn't overstate the model's capabilities
- The performance is comparable to other published results
- No exaggerated language or unsupported assertions are made


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:

After training, the model was able to successfully identify and segment limb images with an IoU of 79.9%.

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:The remaining 22 scans, totaling 6312 images, were fed into the fine-tuned neural network for segmentation.

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.

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. 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.

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:
Looking at the evidence carefully:

1. The claim states "IoU of 79.9%" which is directly supported by multiple mentions in the evidence and clearly shown in the graph (Figure 3) where the test IoU plateaus around 80%.

2. The evidence also provides important context by comparing this performance to DeepLabv3 benchmarks (81.3% and 85.7% on standard datasets), showing the achieved IoU is reasonable.

3. However, the reviewer raises valid concerns about:
- Potential overlap between training/testing data at subject level
- Very small number of ground truth cases (2)
- Quick convergence suggesting possible overfitting
- No data augmentation used

While the numerical claim itself is accurate according to the evidence, these methodological limitations should make us somewhat cautious about its robustness. The claim would be better stated with some of these caveats.

<score>2</score>

The claim gets a score of 2 - it's generally well-stated in terms of the specific metric achieved, but could have been more precise by acknowledging some key limitations in the experimental setup that affect its broader interpretation.


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:

After training, the model was able to successfully identify and segment limb images with an IoU of 79.9%.

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 remaining 22 scans, totaling 6312 images, were fed into the fine-tuned neural network for segmentation.

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.

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. 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.

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:
I'll evaluate the claim "After training, the model was able to successfully identify and segment limb images with an IoU of 79.9%."

This claim is well-stated and accurately reflects the evidence without exaggeration. The evidence directly supports this claim through:
1. The graph showing test IoU stabilizing around 79.9%
2. Explicit statement: "testing IoU remains largely constant, with a value of 79.9% after 10 epochs of training"
3. Contextual validation comparing results to similar studies: "IoU is comparable with values reported by Chen et al. (2017b) which reached peak mIoU values of 81.3%"

The claim states the performance metric straightforwardly without overstating its significance or making unsupported generalizations.

<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:

After training, the model was able to successfully identify and segment limb images with an IoU of 79.9%.

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 remaining 22 scans, totaling 6312 images, were fed into the fine-tuned neural network for segmentation.

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.

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. 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.

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:
Let me evaluate the claim "After training, the model was able to successfully identify and segment limb images with an IoU of 79.9%."

The evidence strongly supports this claim through multiple consistent references:
1. The text explicitly states "the testing IoU remains largely constant, with a value of 79.9% after 10 epochs of training"
2. The graph in Figure 3 visually confirms the test IoU stabilizing around 80%
3. The result is contextualized by comparing it to similar studies (DeepLabv3 achieved 81.3-85.7% on different datasets)

The claim is factual, precise, and does not exaggerate the model's performance. It simply states the achieved metric without overstating implications or making unsupported generalizations.

<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:

The Mean Absolute Error (MAE) of models rendered from images segmented with DL was 0.57 mm \(\pm\) 0.63 mm when compared to models rendered from ground truth images.

The evidence to be evaluated is:In later research, these authors were able to achieve similar accuracy while scanning infant skulls with MAE accuracy values of 0.5 mm \(\pm\) 0.4 mm and 200 images Barbero-Garcia et al. (2020).

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).

Scans 1-11 using the bare liner had slightly better MAE and RMSE values (0.32 mm \(\pm\) 0.51 mm; 2.30 mm \(\pm\) 1.01 mm, respectively) than scans 12-22 with a colored sock over the bare liner (0.77 mm \(\pm\) 0.70 mm; 2.54 mm \(\pm\) 1.21 mm).

Scan 12 had a low MAE value (0.46 mm) but the highest RMSE value (5.54 mm) of all the scans.

Fig. 5C is an example of a limb with low MAE (0.17 mm) and low RMSE (1.26 mm) values.

Since the RMSE errors are systematically impacted by limb flexure, MAE is the more appropriate measure of accuracy in these scans.

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.

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.

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 evaluate the claim and evidence:

The claim states that the MAE was 0.57 mm ± 0.63 mm when comparing DL-segmented models to ground truth models. This is directly supported by multiple pieces of evidence, including the explicit statement "The MAE was found to be on average 0.57 mm ± 0.63 mm." The value is consistently referenced throughout the evidence and is broken down into specific scan results that contribute to this average. The claim makes no exaggerated interpretations and simply states the numerical finding without overreaching conclusions.

<score>1</score>

Justification:
- The claim exactly matches the data presented in the evidence
- The specific value is repeated multiple times in the evidence with consistent reporting
- The claim is purely factual without any interpretative statements
- Supporting details in the evidence show how this average was derived from individual scans
- The claim does not make unsupported generalizations or overstate the significance of the finding


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:

The Mean Absolute Error (MAE) of models rendered from images segmented with DL was 0.57 mm \(\pm\) 0.63 mm when compared to models rendered from ground truth images.

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 later research, these authors were able to achieve similar accuracy while scanning infant skulls with MAE accuracy values of 0.5 mm \(\pm\) 0.4 mm and 200 images Barbero-Garcia et al. (2020).

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).

Scans 1-11 using the bare liner had slightly better MAE and RMSE values (0.32 mm \(\pm\) 0.51 mm; 2.30 mm \(\pm\) 1.01 mm, respectively) than scans 12-22 with a colored sock over the bare liner (0.77 mm \(\pm\) 0.70 mm; 2.54 mm \(\pm\) 1.21 mm).

Scan 12 had a low MAE value (0.46 mm) but the highest RMSE value (5.54 mm) of all the scans.

Fig. 5C is an example of a limb with low MAE (0.17 mm) and low RMSE (1.26 mm) values.

Since the RMSE errors are systematically impacted by limb flexure, MAE is the more appropriate measure of accuracy in these scans.

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.

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.

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 evaluate the claim based on the provided evidence and review comments:

Summary:
The claim states an MAE of 0.57 mm ± 0.63 mm, which is directly supported by multiple pieces of evidence. This specific value appears consistently across the evidence and is broken down further into subgroups (bare liner vs. sock conditions). However, two important limitations highlighted in the review comments affect the strength of this claim:
1. Only 2 ground truth cases were used for comparison
2. Issues with image alignment caused 4 out of 24 scans to fail rendering

While the numerical value itself is accurate, the limited ground truth cases make it difficult to fully trust the generalizability of these results.

<score>2</score>

Justification:
- The claim accurately reports the numerical findings without exaggeration
- The evidence consistently supports the stated MAE value across multiple citations
- However, the claim would be better stated if it acknowledged:
  - The very limited ground truth sample size (2 cases)
  - The failed renderings in 4 scans
- The reviewer's concern about possible cherry-picking due to limited ground truth cases is valid and affects the reliability of the stated accuracy
- The claim is technically accurate but could benefit from more context about these limitations


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:

The Mean Absolute Error (MAE) of models rendered from images segmented with DL was 0.57 mm \(\pm\) 0.63 mm when compared to models rendered from ground truth images.

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 later research, these authors were able to achieve similar accuracy while scanning infant skulls with MAE accuracy values of 0.5 mm \(\pm\) 0.4 mm and 200 images Barbero-Garcia et al. (2020).

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).

Scans 1-11 using the bare liner had slightly better MAE and RMSE values (0.32 mm \(\pm\) 0.51 mm; 2.30 mm \(\pm\) 1.01 mm, respectively) than scans 12-22 with a colored sock over the bare liner (0.77 mm \(\pm\) 0.70 mm; 2.54 mm \(\pm\) 1.21 mm).

Scan 12 had a low MAE value (0.46 mm) but the highest RMSE value (5.54 mm) of all the scans.

Fig. 5C is an example of a limb with low MAE (0.17 mm) and low RMSE (1.26 mm) values.

Since the RMSE errors are systematically impacted by limb flexure, MAE is the more appropriate measure of accuracy in these scans.

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.

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.

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:
I'll evaluate the claim: "The Mean Absolute Error (MAE) of models rendered from images segmented with DL was 0.57 mm ± 0.63 mm when compared to models rendered from ground truth images."

Analysis:
The claim is well-stated and directly supported by multiple pieces of evidence:
1. The exact MAE value (0.57 mm ± 0.63 mm) is explicitly stated in the evidence multiple times
2. The evidence provides detailed breakdowns of MAE values across different scanning conditions
3. The results are comparable to similar research (Barbero-Garcia et al., 2020)
4. The evidence explains why MAE is the appropriate measure over RMSE
5. The claim states the comparison is against ground truth images, which is accurately reflected in the evidence

The claim makes no exaggerations and directly reports the numerical findings without overstating implications or significance.

<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:

The Mean Absolute Error (MAE) of models rendered from images segmented with DL was 0.57 mm \(\pm\) 0.63 mm when compared to models rendered from ground truth images.

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 later research, these authors were able to achieve similar accuracy while scanning infant skulls with MAE accuracy values of 0.5 mm \(\pm\) 0.4 mm and 200 images Barbero-Garcia et al. (2020).

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).

Scans 1-11 using the bare liner had slightly better MAE and RMSE values (0.32 mm \(\pm\) 0.51 mm; 2.30 mm \(\pm\) 1.01 mm, respectively) than scans 12-22 with a colored sock over the bare liner (0.77 mm \(\pm\) 0.70 mm; 2.54 mm \(\pm\) 1.21 mm).

Scan 12 had a low MAE value (0.46 mm) but the highest RMSE value (5.54 mm) of all the scans.

Fig. 5C is an example of a limb with low MAE (0.17 mm) and low RMSE (1.26 mm) values.

Since the RMSE errors are systematically impacted by limb flexure, MAE is the more appropriate measure of accuracy in these scans.

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.

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.

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 stating "The Mean Absolute Error (MAE) of models rendered from images segmented with DL was 0.57 mm ± 0.63 mm when compared to models rendered from ground truth images" is well-stated and directly supported by multiple pieces of evidence. The exact MAE value is consistently reported across the evidence (0.57 mm ± 0.63 mm). The evidence shows this accuracy is comparable to previous research (Barbero-Garcia et al., 2020) and demonstrates consistency across different scanning conditions. The review comment, while noting limited novelty, does not dispute the accuracy claims.

<score>1</score>

Justification:
- The claim precisely matches the reported findings in the evidence without exaggeration
- Multiple sections of evidence confirm the stated MAE value: "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 ± 0.63 mm"
- The accuracy is validated by comparison to similar research: "The average MAE values obtained are excellent (0.57 mm ± 0.63 mm) and comparable with the sub-millimeter MAE accuracy reported by Barbero-Garcia et al. (2020)"
- The claim appropriately focuses on MAE, which the evidence supports as "the more appropriate measure of accuracy in these scans"
- While the reviewer comments on limited novelty and single model usage, these points don't affect the validity of the specific accuracy claim being made