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Abstract #4345

ZTE segmentation of glenohumeral bone structure using deep learning

Michael Carl1, Kaustaub Lall2, Armin Jamshidi2, Eric Chang3,4, Sheronda Statum3,4, Anja Brau1, Christine B Chung3,4, Maggie Fung1, and Won C. Bae3,4
1General Electric Healthcare, Menlo Park, CA, United States, 2Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, United States, 3Radiology, University of California, San Diego, La Jolla, CA, United States, 4Radiology, VA San Diego Healthcare System, San Diego, CA, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Segmentation, ZTE, Deep learningEvaluation of 3D bone morphology of the glenohumeral joint is necessary for pre-surgical planning. Zero echo time (ZTE) MRI provides excellent bone contrast, and we developed a deep learning model to perform automated segmentation of major bones (i.e., humerus and others) from ZTE to aid evaluation. Axial ZTE images of normal shoulders (n=31) acquired at 3T were annotated for training with a 2D U-Net, and the trained model was validated with testing data (n=10 normal shoulder, n=6 symptomatic). Testing accuracy was around 80 to 90% (Dice score) for either cohort, except for a few failed cases with very low scores.

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Keywords