# DenseLes: slice-wise dense network for multiple sclerosis lesion segmentation and classification

## TL;DR

DenseLes is a new method that uses a deep learning model to accurately segment and classify multiple sclerosis lesions in MRI scans.

## Contribution

The paper introduces a slice-wise dense network for improved MS lesion segmentation and classification.

## Key findings

- DenseLes achieved an average Dice score of 0.80% on the Szeged MS dataset.
- On the MSSEG 2016 dataset, Dice scores ranged from 0.32% to 0.73%, matching human raters.
- The method segments lesions in specific brain regions like periventricular and cortical areas.

## Abstract

Accurate and reliable segmentation of multiple sclerosis (MS) lesions from magnetic resonance imaging (MRI) is essential for diagnosis and monitoring disease progression. Therefore, a robust and efficient automated approach can rapidly provide information about the patient. Here, a convolutional neural network-based method is proposed to segment lesions from FLAIR images. The DenseLessystem includes two stages: pre-processing of image data (brain extraction, standardization), then segmentation of MS lesions using an end-to-end slice-wise dense network. We also identified the segmented lesions in specific locations [periventricular, (juxta)cortical, infratentorial, and spinal]. DenseLesis evaluated and compared to other methods on our assembled data and the public MSSEG 2016 MS challenge dataset. Our model demonstrates a significant improvement in segmentation quality over previous approaches, achieving an average Dice score of 0.80% on the Szeged MS dataset. On the MSSEG 2016 dataset, our method achieved Dice scores ranging from 0.32% to 0.73%, comparable to those of human raters.