# A Bibliography of Multiple Sclerosis Lesions Detection Methods using Brain MRIs

arXiv: https://arxiv.org/abs/2302.09516 · HTML: https://mlsanity.com/paper/2302.09516/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 235 references that are in the map.
- First posted on arXiv 19 Feb 2023.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning](https://mlsanity.com/paper/1602.03409/) (2016)
- [Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation](https://mlsanity.com/paper/1808.01200/) (2018)
- [Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregation](https://mlsanity.com/paper/1505.03046/) (2015)
- [Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection](https://mlsanity.com/paper/1803.11078/) (2018)
- [One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks](https://mlsanity.com/paper/1805.12415/) (2018)
- [Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach](https://mlsanity.com/paper/1702.04869/) (2017)
- [Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation](https://mlsanity.com/paper/1811.02942/) (2018)
- [Multiple Sclerosis Lesion Synthesis in MRI using an encoder-decoder U-NET](https://mlsanity.com/paper/1901.05733/) (2019)
- [Multiple Sclerosis Lesion Segmentation from Brain MRI via Fully Convolutional Neural Networks](https://mlsanity.com/paper/1803.09172/) (2018)
- [Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis](https://mlsanity.com/paper/1809.03185/) (2018)
- [A Self-Adaptive Network For Multiple Sclerosis Lesion Segmentation From Multi-Contrast MRI With Various Imaging Protocols](https://mlsanity.com/paper/1811.07491/) (2018)
- [Multiple Sclerosis Lesion Activity Segmentation with Attention-Guided Two-Path CNNs](https://mlsanity.com/paper/2008.02001/) (2020)
- [Multi-Scale Convolutional-Stack Aggregation for Robust White Matter Hyperintensities Segmentation](https://mlsanity.com/paper/1807.05153/) (2018)
- [Multiple sclerosis lesion enhancement and white matter region estimation using hyperintensities in FLAIR images](https://mlsanity.com/paper/1807.09619/) (2018)

## Abstract (arXiv)

Introduction: Multiple Sclerosis (MS) is a chronic disease that affects millions of people across the globe. MS can critically affect different organs of the central nervous system such as the eyes, the spinal cord, and the brain. Background: To help physicians in diagnosing MS lesions, computer-aided methods are widely used. In this regard, a considerable research has been carried out in the area of automatic detection and segmentation of MS lesions in magnetic resonance images (MRIs). Methodology: In this study, we review the different approaches that have been used in computer-aided detection and segmentation of MS lesions. Our review resulted in categorizing MS lesion segmentation approaches into six broad categories: data-driven, statistical, supervised machine learning, unsupervised machine learning, fuzzy, and deep learning-based techniques. We critically analyze the different techniques under these approaches and highlight their strengths and weaknesses. Results: From the study, we observe that a considerable amount of work, around 25% of related literature, is focused on statistical-based MS lesion segmentation techniques, followed by 21.15% for data-driven based methods, 19.23% for deep learning and 15.38% for supervised methods. Implication: The study points out the challenges/gaps to be addressed in future research. The study shows the work which has been done in last one decade in detection and segmentation of MS lesions. The results show that, in recent years, deep learning methods are outperforming all the others methods.