MRI Segmentation of Musculoskeletal Components Using U-Net: Preliminary Results

Abstract

Recent advances in medical imaging and computer vision offer unprecedented potential for objective, automated, and personalized diagnosis and treatment in healthcare. A pivotal area where this potential is yet to be fully harnessed lies in the processing of MRI data for the extraction of musculoskeletal features, crucial for patient-specific musculoskeletal modeling. Such models hold significance for assessing neuromusculoskeletal diseases and analyzing human movement. This manuscript presents our initial efforts in developing a method that utilizes deep learning to segment specific anatomical structures, namely osseous and myeloid tissues, from MRI scans, with minimal annotated data. We place particular emphasis on a convolutional neural network (CNN) approach, utilizing the U-Net architecture. Our work elaborates on the segmentation process, demonstrates results on individual MRI slices, and proposes a method for volumetric analysis. We also explore potential enhancements for achieving more precise segmentations and robust feature extraction. The promising initial findings advocate for a future where the segmentation of intricate anatomical structures becomes more accessible, efficient, and rapid.

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