# Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation

arXiv: https://arxiv.org/abs/1811.02942 · HTML: https://mlsanity.com/paper/1811.02942/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2018.

## Built on by (most cited first, 16 of 16)

- [Multiple Sclerosis Lesion Analysis in Brain Magnetic Resonance Images: Techniques and Clinical Applications](https://mlsanity.com/paper/2104.10029/) (2021)
- [Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review](https://mlsanity.com/paper/2105.04881/) (2021)
- [Hyper-Convolution Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/2105.10559/) (2021)
- [Spatially Covariant Lesion Segmentation](https://mlsanity.com/paper/2301.07895/) (2023)
- [Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs](https://mlsanity.com/paper/2103.07240/) (2021)
- [Self-supervised Lesion Change Detection and Localisation in Longitudinal Multiple Sclerosis Brain Imaging](https://mlsanity.com/paper/2106.00919/) (2021)
- [Interactive Segmentation for COVID-19 Infection Quantification on Longitudinal CT scans](https://mlsanity.com/paper/2110.00948/) (2021)
- [Multiple Sclerosis Lesions Segmentation using Attention-Based CNNs in FLAIR Images](https://mlsanity.com/paper/2201.01832/) (2022)
- [Hyper-Convolutions via Implicit Kernels for Medical Imaging](https://mlsanity.com/paper/2202.02701/) (2022)
- [UNISELF: A Unified Network with Instance Normalization and Self-Ensembled Lesion Fusion for Multiple Sclerosis Lesion Segmentation](https://mlsanity.com/paper/2508.03982/) (2025)
- [Latent Space Analysis of VAE and Intro-VAE applied to 3-dimensional MR Brain Volumes of Multiple Sclerosis, Leukoencephalopathy, and Healthy Patients](https://mlsanity.com/paper/2101.06772/) (2021)
- [Ensemble CNN and Uncertainty Modeling to Improve Automatic Identification/Segmentation of Multiple Sclerosis Lesions in Magnetic Resonance Imaging](https://mlsanity.com/paper/2108.11791/) (2021)
- [A Bibliography of Multiple Sclerosis Lesions Detection Methods using Brain MRIs](https://mlsanity.com/paper/2302.09516/) (2023)
- [DMC-Net: Lightweight Dynamic Multi-Scale and Multi-Resolution Convolution Network for Pancreas Segmentation in CT Images](https://mlsanity.com/paper/2410.02129/) (2024)
- [VeMo: A Lightweight Data-Driven Approach to Model Vehicle Dynamics](https://mlsanity.com/paper/2510.07447/) (2025)
- [Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models](https://mlsanity.com/paper/2605.09666/) (2026)

## Abstract (arXiv)

In this paper, we present an automated approach for segmenting multiple sclerosis (MS) lesions from multi-modal brain magnetic resonance images. Our method is based on a deep end-to-end 2D convolutional neural network (CNN) for slice-based segmentation of 3D volumetric data. The proposed CNN includes a multi-branch downsampling path, which enables the network to encode information from multiple modalities separately. Multi-scale feature fusion blocks are proposed to combine feature maps from different modalities at different stages of the network. Then, multi-scale feature upsampling blocks are introduced to upsize combined feature maps to leverage information from lesion shape and location. We trained and tested the proposed model using orthogonal plane orientations of each 3D modality to exploit the contextual information in all directions. The proposed pipeline is evaluated on two different datasets: a private dataset including 37 MS patients and a publicly available dataset known as the ISBI 2015 longitudinal MS lesion segmentation challenge dataset, consisting of 14 MS patients. Considering the ISBI challenge, at the time of submission, our method was amongst the top performing solutions. On the private dataset, using the same array of performance metrics as in the ISBI challenge, the proposed approach shows high improvements in MS lesion segmentation compared with other publicly available tools.