Abstract: The presence of a vein inside white matter lesions was recently proposed as an imaging biomarker that can help in the differential diagnosis of Multiple Sclerosis (MS), potentially reducing the challenging clinical-radiological gap. Here, we propose a prototype based on ensembling small 3D convolutional networks to classify perivenular (P+) and non-perivenular (P-) lesions. Even without prior lesion masking, our approach reaches performance superior to imaging filters designed specifically to detect blood vessels, and that have access to a lesion mask.
Keywords: WM Lesions, Central Vein Sign, Automated Detection, Ensemble 3D CNN
Author Affiliation: Advanced Clinical Imaging Technology (ACIT), Siemens Healthcare AG, Lausanne, Switzerland