Factorisation-based Image Labelling

• URL: http://arxiv.org/abs/2111.10326v1
• Date: Fri, 19 Nov 2021 17:10:54 GMT
• Title: Factorisation-based Image Labelling
• Authors: Yu Yan, Yael Balbastre, Mikael Brudfors, John Ashburner
• Abstract summary: We propose a patched-based label propagation approach based on a generative model with latent variables. We compare our proposed model against the state-of-the-art using data from the MICCAI 2012 Grand Challenge and Workshop on Multi-Atlas Labeling.
• Score: 0.9319432628663639
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 label 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 Labeling. 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.

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