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

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Serval ID
serval:BIB_6CDC75E672E9
Type
Inproceedings: an article in a conference proceedings.
Collection
Publications
Institution
Title
Automated Detection of Cortical Lesions in Multiple Sclerosis Patients with 7T MRI
Title of the conference
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
Author(s)
La Rosa Francesco, Beck Erin S., Abdulkadir Ahmed, Thiran Jean-Philippe, Reich Daniel S., Sati Pascal, Bach Cuadra Meritxell
Publisher
Prof. Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Prof. Leo Joskowicz
Organization
23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part IV
Publication state
Published
Issued date
04/10/2020
Peer-reviewed
Oui
Language
english
Abstract
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.
Keywords
MRI, Ultra-high field, Multiple Sclerosis, Cortical lesions, Segmentation, CNN
Create date
05/02/2021 16:43
Last modification date
06/02/2021 7:09
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