Keywords: Machine Learning/Artificial Intelligence, SegmentationWhole-Body Diffusion Weighted Imaging (WBDWI) requires automated tools that delineate malignant bone disease based on high b-value signal intensity, leading to state-of-the-art imaging biomarkers of response. As an initial step, we have developed an automated deep-learning pipeline that automatically delineates the skeleton from WBDWI. Our approach is trained on paired examples, where ground truth is defined through a set of weak labels (non-binary segmentations) derived from a computationally expensive atlas-based segmentation approach. The model showed on average a dice score, precision and recall between the manual and derived skeleton segmentations on test datasets of 0.74, 0.78, and 0.7, respectively.
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