# DeepLesionBrain: Towards a broader deep-learning generalization for multiple sclerosis lesion segmentation

arXiv: https://arxiv.org/abs/2012.07950 · HTML: https://mlsanity.com/paper/2012.07950/

## Key facts

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

## Built on by (most cited first, 15 of 15)

- [Domain Generalization for Medical Image Analysis: A Review](https://mlsanity.com/paper/2310.08598/) (2023)
- [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)
- [A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation](https://mlsanity.com/paper/2204.02779/) (2022)
- [A Survey on Domain Generalization for Medical Image Analysis](https://mlsanity.com/paper/2402.05035/) (2024)
- [Towards an accurate and generalizable multiple sclerosis lesion segmentation model using self-ensembled lesion fusion](https://mlsanity.com/paper/2312.01460/) (2023)
- [Towards better Interpretable and Generalizable AD detection using Collective Artificial Intelligence](https://mlsanity.com/paper/2206.03247/) (2022)
- [Navigating Distribution Shifts in Medical Image Analysis: A Survey](https://mlsanity.com/paper/2411.05824/) (2024)
- [Longitudinal detection of new MS lesions using Deep Learning](https://mlsanity.com/paper/2206.08272/) (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)
- [Toward Generalizable Multiple Sclerosis Lesion Segmentation Models](https://mlsanity.com/paper/2410.19623/) (2024)
- [Learning to segment anatomy and lesions from disparately labeled sources in brain MRI](https://mlsanity.com/paper/2503.18840/) (2025)
- [FLAIRBrainSeg: Fine-grained brain segmentation using FLAIR MRI only](https://mlsanity.com/paper/2504.03376/) (2025)
- [petBrain: A New Pipeline for Amyloid, Tau Tangles and Neurodegeneration Quantification Using PET and MRI](https://mlsanity.com/paper/2506.03217/) (2025)
- [FedDAPL: Toward Client-Private Generalization in Federated Learning](https://mlsanity.com/paper/2509.23688/) (2025)
- [CAHAL: Clinically Applicable resolution enHAncement for Low-resolution MRI scans](https://mlsanity.com/paper/2604.18781/) (2026)

## Abstract (arXiv)

Recently, segmentation methods based on Convolutional Neural Networks (CNNs) showed promising performance in automatic Multiple Sclerosis (MS) lesions segmentation. These techniques have even outperformed human experts in controlled evaluation conditions such as Longitudinal MS Lesion Segmentation Challenge (ISBI Challenge). However state-of-the-art approaches trained to perform well on highly-controlled datasets fail to generalize on clinical data from unseen datasets. Instead of proposing another improvement of the segmentation accuracy, we propose a novel method robust to domain shift and performing well on unseen datasets, called DeepLesionBrain (DLB). This generalization property results from three main contributions. First, DLB is based on a large group of compact 3D CNNs. This spatially distributed strategy ensures a robust prediction despite the risk of generalization failure of some individual networks. Second, DLB includes a new image quality data augmentation to reduce dependency to training data specificity (e.g., acquisition protocol). Finally, to learn a more generalizable representation of MS lesions, we propose a hierarchical specialization learning (HSL). HSL is performed by pre-training a generic network over the whole brain, before using its weights as initialization to locally specialized networks. By this end, DLB learns both generic features extracted at global image level and specific features extracted at local image level. DLB generalization was validated in cross-dataset experiments on MSSEG'16, ISBI challenge, and in-house datasets. During experiments, DLB showed higher segmentation accuracy, better segmentation consistency and greater generalization performance compared to state-of-the-art methods. Therefore, DLB offers a robust framework well-suited for clinical practice.