Title: Image Segmentation using Transfer Learning with DeepLabv3 to Facilitate Photogrammetric Limb Scanning. Abstract: In this paper, we explore the use of deep learning (DL) in conjunction with photogrammetry for scanning amputated limbs. Combining these two technologies can expand the scope of prosthetic telemedicine by facilitating low-cost limb scanning using cell phones. Previous research identified image segmentation as one of the main limitations of using photogrammetry for limb scanning. Based on those limitations, this work sought to answer two main research questions: (1) Can a neural network be trained to identify and segment an amputated limb automatically? (2) Will segmenting 2D limb images using neural networks impact the accuracy of 3D models generated via photogrammetry? To answer the first question, transfer learning was applied to a neural network with the DeepLabv3 architecture. After training, the model was able to successfully identify and segment limb images with an IoU of 79.9%. To answer the second question, the fine-tuned DL model was applied to a dataset of 22 scans comprising 6312 limb images, then 3D models were rendered utilizing Agisoft Metashape. 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. These results are important because segmentation with DL makes photogrammetry for limb scanning feasible on a large clinical scale. Future work should focus on generalizing the segmentation model for different types of amputations and imaging conditions.