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Abstract #3262

Machine Learning Automatic Segmentation of Spinal Cord Lesions in Multiple Sclerosis Patients

Peter Hsu1, Sindhuja Govindarajan1, Nikhil Chettipally1, Lev Bangiyev2, Robert Peyster2, Giuseppe Cruciata2, Patricia Coyle2, Haifang Li2, Hasan Saffiudin1, Ryan Merritt1, Eric Wei1, Almighty Ironnah1, and Kwan Chen1
1Stony Brook University, Stony Brook, NY, United States, 2Stony Brook University Hospital, Stony Brook, NY, United States

Multiple Sclerosis lesions in the spinal cord are associated with more debilitative disease outcomes and have predictive value for prognosis and diagnosis. However, these lesions are difficult to detect from MRI scans and this process is susceptible to inter-rater and intra-rater variability. Machine Learning techniques have the ability to assist in this problem. We propose a Convolutional Neural Network that can perform accurate identification and segmentation of MS lesions in the spinal cord. This method achieves high overlap with the segmentations of attending radiologists and is robust to imaging artifacts, showcasing the potential to be a tool for clinical practice.

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