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

arXiv: https://arxiv.org/abs/2008.06780 · HTML: https://mlsanity.com/paper/2008.06780/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2020.

## Built on by (most cited first, 4 of 4)

- [ModDrop++: A Dynamic Filter Network with Intra-subject Co-training for Multiple Sclerosis Lesion Segmentation with Missing Modalities](https://mlsanity.com/paper/2203.04959/) (2022)
- [Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review](https://mlsanity.com/paper/2105.04881/) (2021)
- [Cortical lesions, central vein sign, and paramagnetic rim lesions in multiple sclerosis: emerging machine learning techniques and future avenues](https://mlsanity.com/paper/2201.07463/) (2022)
- [Using Deep Image Prior to Assist Variational Selective Segmentation Deep Learning Algorithms](https://mlsanity.com/paper/2112.00793/) (2021)

## Abstract (arXiv)

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.