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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