AUTHOR=Yan Yu , Balbastre Yaël , Brudfors Mikael , Ashburner John 

TITLE=Factorisation-Based Image Labelling

JOURNAL=Frontiers in Neuroscience

VOLUME=15

YEAR=2022

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2021.818604

DOI=10.3389/fnins.2021.818604

ISSN=1662-453X

ABSTRACT=<p>Segmentation of brain magnetic resonance images (MRI) into anatomical regions is a useful task in neuroimaging. Manual annotation is time consuming and expensive, so having a fully automated and general purpose brain segmentation algorithm is highly desirable. To this end, we propose a patched-based labell propagation approach based on a generative model with latent variables. Once trained, our Factorisation-based Image Labelling (FIL) model is able to label target images with a variety of image contrasts. We compare the effectiveness of our proposed model against the state-of-the-art using data from the <italic>MICCAI 2012 Grand Challenge and Workshop on Multi-Atlas Labelling</italic>. As our approach is intended to be general purpose, we also assess how well it can handle domain shift by labelling images of the same subjects acquired with different MR contrasts.</p>