# Multiple Sclerosis Lesions Segmentation using Attention-Based CNNs in FLAIR Images

arXiv: https://arxiv.org/abs/2201.01832 · HTML: https://mlsanity.com/paper/2201.01832/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 68 references that are in the map.
- First posted on arXiv 5 Jan 2022.

## Builds on (most cited first, 22 of 22)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [Brain Tumor Segmentation with Deep Neural Networks](https://mlsanity.com/paper/1505.03540/) (2015)
- [A review: Deep learning for medical image segmentation using multi-modality fusion](https://mlsanity.com/paper/2004.10664/) (2020)
- [Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond](https://mlsanity.com/paper/2102.01998/) (2021)
- [Equilibrated adaptive learning rates for non-convex optimization](https://mlsanity.com/paper/1502.04390/) (2015)
- [Automatic segmentation of MR brain images with a convolutional neural network](https://mlsanity.com/paper/1704.03295/) (2017)
- [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)
- [Joint Sequence Learning and Cross-Modality Convolution for 3D Biomedical Segmentation](https://mlsanity.com/paper/1704.07754/) (2017)
- [Multiple Sclerosis Lesion Synthesis in MRI using an encoder-decoder U-NET](https://mlsanity.com/paper/1901.05733/) (2019)
- [M3d-CAM: A PyTorch library to generate 3D data attention maps for medical deep learning](https://mlsanity.com/paper/2007.00453/) (2020)
- [Explainable AI For COVID-19 CT Classifiers: An Initial Comparison Study](https://mlsanity.com/paper/2104.14506/) (2021)
- [3D AGSE-VNet: An Automatic Brain Tumor MRI Data Segmentation Framework](https://mlsanity.com/paper/2107.12046/) (2021)
- [Multiple Sclerosis Lesion Segmentation from Brain MRI via Fully Convolutional Neural Networks](https://mlsanity.com/paper/1803.09172/) (2018)
- [Medial Spectral Coordinates for 3D Shape Analysis](https://mlsanity.com/paper/2111.13295/) (2021)

## Built on by (most cited first, 2 of 2)

- [Toward Generalizable Multiple Sclerosis Lesion Segmentation Models](https://mlsanity.com/paper/2410.19623/) (2024)
- [Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models](https://mlsanity.com/paper/2605.09666/) (2026)

## Abstract (arXiv)

Objective: Multiple Sclerosis (MS) is an autoimmune, and demyelinating disease that leads to lesions in the central nervous system. This disease can be tracked and diagnosed using Magnetic Resonance Imaging (MRI). Up to now a multitude of multimodality automatic biomedical approaches is used to segment lesions which are not beneficial for patients in terms of cost, time, and usability. The authors of the present paper propose a method employing just one modality (FLAIR image) to segment MS lesions accurately. Methods: A patch-based Convolutional Neural Network (CNN) is designed, inspired by 3D-ResNet and spatial-channel attention module, to segment MS lesions. The proposed method consists of three stages: (1) the contrast-limited adaptive histogram equalization (CLAHE) is applied to the original images and concatenated to the extracted edges in order to create 4D images; (2) the patches of size 80 * 80 * 80 * 2 are randomly selected from the 4D images; and (3) the extracted patches are passed into an attention-based CNN which is used to segment the lesions. Finally, the proposed method was compared to previous studies of the same dataset. Results: The current study evaluates the model, with a test set of ISIB challenge data. Experimental results illustrate that the proposed approach significantly surpasses existing methods in terms of Dice similarity and Absolute Volume Difference while the proposed method use just one modality (FLAIR) to segment the lesions. Conclusions: The authors have introduced an automated approach to segment the lesions which is based on, at most, two modalities as an input. The proposed architecture is composed of convolution, deconvolution, and an SCA-VoxRes module as an attention module. The results show, the proposed method outperforms well compare to other methods.