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Abstract #1135

DOSMA: A deep-learning, open-source framework for musculoskeletal MRI analysis

Arjun D. Desai1, Marco Barbieri1,2, Valentina Mazzoli1, Elka Rubin1, Marianne S. Black1,3, Lauren E. Watkins4, Garry E. Gold1,4,5, Brian A. Hargreaves1,4,6, and Akshay S. Chaudhari1

1Radiology, Stanford University, Stanford, CA, United States, 2Physics and Astronomy, University of Bologna, Bologna, Italy, 3Mechanical Engineering, Stanford University, Stanford, CA, United States, 4Bioengineering, Stanford University, Stanford, CA, United States, 5Orthopedic Surgery, Stanford University, Stanford, CA, United States, 6Electrical Engineering, Stanford University, Stanford, CA, United States

With the onset of deep learning, many novel post-processing networks are emerging. However, these algorithms are decentralized, and thus are difficult to consolidate and implement in a clinical setting. To this end, we have developed a Python-based deep-learning, open-source musculoskeletal MR analysis framework termed DOSMA to reduce the complexity of tissue segmentation, registration, and quantitative analysis. We hope that by encouraging contributions from many researchers, this pipeline will facilitate automating traditionally laborious, manual, decentralized MR image processing tasks and will provide a standardized comparison framework for different quantitative estimation techniques.

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