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Automated Detection of Cortical Lesions in Multiple Sclerosis Patients with 7T MRI

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Document pages: 11 pages

Abstract: The automated detection of cortical lesions (CLs) in patients with multiplesclerosis (MS) is a challenging task that, despite its clinical relevance, hasreceived very little attention. Accurate detection of the small and scarcelesions requires specialized sequences and high or ultra-high field MRI. Forsupervised training based on multimodal structural MRI at 7T, two expertsgenerated ground truth segmentation masks of 60 patients with 2014 CLs. Weimplemented a simplified 3D U-Net with three resolution levels (3D U-Net-). Byincreasing the complexity of the task (adding brain tissue segmentation), whilerandomly dropping input channels during training, we improved the performancecompared to the baseline. Considering a minimum lesion size of 0.75 { mu}L, weachieved a lesion-wise cortical lesion detection rate of 67 and a falsepositive rate of 42 . However, 393 (24 ) of the lesions reported as falsepositives were post-hoc confirmed as potential or definite lesions by anexpert. This indicates the potential of the proposed method to support expertsin the tedious process of CL manual segmentation.

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