Scanner Invariant Multiple Sclerosis Lesion Segmentation from MRI
This addresses domain shift issues in multiple sclerosis lesion segmentation from MRI data, which is incremental as it builds on existing encoder-decoder methods with added regularization.
The paper tackles the problem of MRI segmentation across multiple scanning sites affected by domain shifts by integrating a traditional encoder-decoder network with a regularization network and an auxiliary loss term, resulting in better generalization performance on a dataset of 117 patients from 56 sites.
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.