Factorisation-Based Image Labelling

Frontiers in Neuroscience

John Ashburner³  Yu Yan³  Yaël Balbastre⁴  Mikael Brudfors⁴ 

[1] Imaging Sciences, King's College London, London, United Kingdom;Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States;;School of Biomedical Engineering &Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom;

关键词: label propagation;    atlas;    machine learning;    latent variables;    variational bayes;   

DOI  :  10.3389/fnins.2021.818604

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown