# Scanner Invariant Multiple Sclerosis Lesion Segmentation from MRI

arXiv: https://arxiv.org/abs/1910.10035 · HTML: https://mlsanity.com/paper/1910.10035/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2019.

## Built on by (most cited first, 12 of 12)

- [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021)
- [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)
- [Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation](https://mlsanity.com/paper/2206.01369/) (2022)
- [Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges](https://mlsanity.com/paper/2309.07438/) (2023)
- [Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review](https://mlsanity.com/paper/2105.04881/) (2021)
- [Towards an accurate and generalizable multiple sclerosis lesion segmentation model using self-ensembled lesion fusion](https://mlsanity.com/paper/2312.01460/) (2023)
- [MAP: Domain Generalization via Meta-Learning on Anatomy-Consistent Pseudo-Modalities](https://mlsanity.com/paper/2309.01286/) (2023)
- [Harmonization and the Worst Scanner Syndrome](https://mlsanity.com/paper/2101.06255/) (2021)
- [UNISELF: A Unified Network with Instance Normalization and Self-Ensembled Lesion Fusion for Multiple Sclerosis Lesion Segmentation](https://mlsanity.com/paper/2508.03982/) (2025)
- [Single Domain Generalization in Diabetic Retinopathy: A Neuro-Symbolic Learning Approach](https://mlsanity.com/paper/2509.02918/) (2025)
- [MDT-Net: Multi-domain Transfer by Perceptual Supervision for Unpaired Images in OCT Scan](https://mlsanity.com/paper/2203.06363/) (2022)
- [VesselMorph: Domain-Generalized Retinal Vessel Segmentation via Shape-Aware Representation](https://mlsanity.com/paper/2307.00240/) (2023)

## Abstract (arXiv)

This paper presents a simple and effective generalization method for magnetic resonance imaging (MRI) segmentation when data is collected from multiple MRI scanning sites and as a consequence is affected by (site-)domain shifts. We propose to integrate a traditional encoder-decoder network with a regularization network. This added network includes an auxiliary loss term which is responsible for the reduction of the domain shift problem and for the resulting improved generalization. The proposed method was evaluated on multiple sclerosis lesion segmentation from MRI data. We tested the proposed model on an in-house clinical dataset including 117 patients from 56 different scanning sites. In the experiments, our method showed better generalization performance than other baseline networks.