serval:BIB_6CDC75E672E9
Automated Detection of Cortical Lesions in Multiple Sclerosis Patients with 7T MRI
https://www.springerprofessional.de/en/automated-detection-of-cortical-lesions-in-multiple-sclerosis-pa/18443222
La Rosa
Francesco
author
Beck
Erin S.
author
Abdulkadir
Ahmed
author
Thiran
Jean-Philippe
author
Reich
Daniel S.
author
Sati
Pascal
author
Bach Cuadra
Meritxell
author
23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part IV
organizer of meeting
inproceedings
2020-10-04
Prof. Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Prof. Leo Joskowicz
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
conference publication
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 μL, 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.
MRI, Ultra-high field, Multiple Sclerosis, Cortical lesions, Segmentation, CNN
eng
60_published
peer-reviewed
University of Lausanne
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