Abstract #1135

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

Arjun D. Desai¹, Marco Barbieri^1,2, Valentina Mazzoli¹, Elka Rubin¹, Marianne S. Black^1,3, Lauren E. Watkins⁴, Garry E. Gold^1,4,5, Brian A. Hargreaves^1,4,6, 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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