Factorisation-Based Image Labelling

• Published in: Frontiers in neuroscience Vol. 15; p. 818604
• Main Authors: Yan, Yu, Balbastre, Yaël, Brudfors, Mikael, Ashburner, John
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 17.01.2022; Frontiers Media S.A
• Subjects: Algorithms; Automation; Image processing; label propagation; Labeling; latent variables; machine learning; Magnetic resonance imaging; Neural networks; Neuroimaging; Neuroscience; Pattern recognition; Propagation; Registration; Segmentation; variational bayes; label propagation; latent variables; machine learning; atlas; variational bayes
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2021.818604

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 MICCAI 2012 Grand Challenge and Workshop on Multi-Atlas Labelling . 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.