A persistent homology-based topological loss function for multi-class CNN segmentation of cardiac MRI

• URL: http://arxiv.org/abs/2008.09585v1
• Date: Fri, 21 Aug 2020 17:09:13 GMT
• Title: A persistent homology-based topological loss function for multi-class CNN segmentation of cardiac MRI
• Authors: Nick Byrne, James R. Clough, Giovanni Montana, Andrew P. King
• Abstract summary: We build a richer description of segmentation topology by considering all possible labels and label pairs. These topological priors allow us to resolve all topological errors in a subset of 150 examples from the ACDC short axis CMR training data set.
• Score: 7.993897173085253
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With respect to spatial overlap, CNN-based segmentation of short axis cardiovascular magnetic resonance (CMR) images has achieved a level of performance consistent with inter observer variation. However, conventional training procedures frequently depend on pixel-wise loss functions, limiting optimisation with respect to extended or global features. As a result, inferred segmentations can lack spatial coherence, including spurious connected components or holes. Such results are implausible, violating the anticipated topology of image segments, which is frequently known a priori. Addressing this challenge, published work has employed persistent homology, constructing topological loss functions for the evaluation of image segments against an explicit prior. Building a richer description of segmentation topology by considering all possible labels and label pairs, we extend these losses to the task of multi-class segmentation. These topological priors allow us to resolve all topological errors in a subset of 150 examples from the ACDC short axis CMR training data set, without sacrificing overlap performance.

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