Bone and bone marrow segmentation from hip 3T magnetic resonance imaging by using local salient anatomy structure to guide deep learning-based object localization and delineation

Yubing Tong; Zihan Huang; Jayaram K. Udupa; Leihui Tong; Brandon C. Jones; Gregory Chang; Drew A. Torigian; Chamith S. Rajapakse

doi:10.1117/12.2612913

4 April 2022 Bone and bone marrow segmentation from hip 3T magnetic resonance imaging by using local salient anatomy structure to guide deep learning-based object localization and delineation

Yubing Tong, Zihan Huang, Jayaram K. Udupa, Leihui Tong, Brandon C. Jones, Gregory Chang, Drew A. Torigian, Chamith S. Rajapakse

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Proceedings Volume 12034, Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling; 120341Z (2022) https://doi.org/10.1117/12.2612913
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

Currently, 3T hip MRI can be used to estimate femur strength and cortical bone thickness. One of the major hurdles in this application is that objects (osseous structures) are manually segmented which involves significant human labor. In this study, we propose an automatic and accurate algorithm for osseous structure segmentation from hip 3T MRI by using a deep convolutional neural network. The approach includes two stages: 1) automatic localization of acetabulum and femur by using the femoral head as a reference, and 2) 2D bounding box (BB) set up for each object based on the localization information from femoral head followed by a UNet to segment the target object within the BB. 90 3T hip MRI image data sets were utilized in this study that were divided into training, validating, and testing groups (60%:20%:20%), and a 5-fold cross-validation was adopted in the procedure. The study showed that automated segmentation results were comparable to the reference standard from manual segmentation. The average Dice Coefficient for acetabular and femoral (i.e., cortical and medullary bone plus bone marrow) segmentation was 0.93 and 0.96, respectively. Segmentations of acetabular and femoral medullary cavity (i.e., medullary bone plus bone marrow) had Dice Coefficient of 0.89 and 0.95, respectively. Acetabular and femoral cortical bone segmentations were more challenging with lower Dice Coefficient of around 0.7. The proposed approach is automatic and effective without any interaction from humans. The idea of using local salient anatomy to guide object localization approaches is heuristic and can be easily generalized to other localization problems in practice.

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Yubing Tong, Zihan Huang, Jayaram K. Udupa, Leihui Tong, Brandon C. Jones, Gregory Chang, Drew A. Torigian, and Chamith S. Rajapakse "Bone and bone marrow segmentation from hip 3T magnetic resonance imaging by using local salient anatomy structure to guide deep learning-based object localization and delineation", Proc. SPIE 12034, Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling, 120341Z (4 April 2022); https://doi.org/10.1117/12.2612913

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