Automated Detection of Cortical Lesions in Multiple Sclerosis Patients with 7T MRI
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The automated detection of cortical lesions (CLs) in patients with multiple sclerosis (MS) is a challenging task that, despite its clinical relevance, has received very little attention. Accurate detection of the small and scarce lesions requires specialized sequences and high or ultra-high field MRI. For supervised training based on multimodal structural MRI at 7T, two experts generated ground truth segmentation masks of 60 patients with 2014 CLs. We implemented a simplified 3D U-Net with three resolution levels (3D U-Net-). By increasing the complexity of the task (adding brain tissue segmentation), while randomly dropping input channels during training, we improved the performance compared to the baseline. Considering a minimum lesion size of 0.75 \(\upmu \)L, we achieved a lesion-wise cortical lesion detection rate of 67% and a false positive rate of 42%. However, 393 (24%) of the lesions reported as false positives were post-hoc confirmed as potential or definite lesions by an expert. This indicates the potential of the proposed method to support experts in the tedious process of CL manual segmentation.
KeywordsMRI Ultra-high field Multiple sclerosis Cortical lesions Segmentation CNN
This project is supported by the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie project TRABIT (agreement No 765148), the Centre d\('\)Imagerie BioMédicale of the University of Lausanne, the Swiss Federal Institute of Technology Lausanne, the University of Geneva, the Centre Hospitalier Universitaire Vaudois, and the Hôpitaux Universitaires de Genève. Erin S Beck is supported by a Career Transition Fellowship from the National Multiple Sclerosis Society. Pascal Sati, Erin S Beck, and Daniel S Reich are supported by the Intramural Research Program of the National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA. Ahmed Abdulkadir is supported by the Swiss National Science Foundation grant SNSF 173880.
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