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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

Machine Learning techniques have the ability to identify MS lesions in the spinal cord from MR images. We propose a Convolutional Neural Network that can perform fast and accurate segmentation of spinal cord lesions with high overlap compared to attending radiologists.

Segmentations made by our model, SCT, and three radiology residents in comparison to the consensus ground truth on an MR image of the spine with lesions. The DSC for this case is highlighted for each rater.

Comparison of the U-Net++, SCT, and three radiology residents on the 20 testing images of the cervical spinal cord. 15 images had lesions present and 5 had no lesions. Some control cases had other imaging artifacts to represent difficult or uncertain cases. The best performance is highlighted in bold.