AUTHOR=Ackaouy Antoine , Courty Nicolas , Vallée Emmanuel , Commowick Olivier , Barillot Christian , Galassi Francesca TITLE=Unsupervised Domain Adaptation With Optimal Transport in Multi-Site Segmentation of Multiple Sclerosis Lesions From MRI Data JOURNAL=Frontiers in Computational Neuroscience VOLUME=14 YEAR=2020 URL=https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2020.00019 DOI=10.3389/fncom.2020.00019 ISSN=1662-5188 ABSTRACT=

Automatic segmentation of Multiple Sclerosis (MS) lesions from Magnetic Resonance Imaging (MRI) images is essential for clinical assessment and treatment planning of MS. Recent years have seen an increasing use of Convolutional Neural Networks (CNNs) for this task. Although these methods provide accurate segmentation, their applicability in clinical settings remains limited due to a reproducibility issue across different image domains. MS images can have highly variable characteristics across patients, MRI scanners and imaging protocols; retraining a supervised model with data from each new domain is not a feasible solution because it requires manual annotation from expert radiologists. In this work, we explore an unsupervised solution to the problem of domain shift. We present a framework, Seg-JDOT, which adapts a deep model so that samples from a source domain and samples from a target domain sharing similar representations will be similarly segmented. We evaluated the framework on a multi-site dataset, MICCAI 2016, and showed that the adaptation toward a target site can bring remarkable improvements in a model performance over standard training.