MRI Segmentation of Musculoskeletal Components Using U-Net: Preliminary Results.
International Conference on Bioscience, Biochemistry and Bioinformatics(2024)
摘要
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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