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Clough, J.* ; Byrne, N.* ; Öksüz, I.* ; Zimmer, V.A.* ; Schnabel, J.A.* ; King, A.*

A Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homology.

IEEE Trans. Pattern Anal. Mach. Intell., DOI: 10.1109/TPAMI.2020.3013679 (2020)
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Open Access Hybrid
We introduce a method for training neural networks to perform image or volume segmentation in which prior knowledge about the topology of the segmented object can be explicitly provided and then incorporated into the training process. By using the differentiable properties of persistent homology, a concept used in topological data analysis, we can specify the desired topology of segmented objects in terms of their Betti numbers and then drive the proposed segmentations to contain the specified topological features. Importantly this process does not require any ground-truth labels, just prior knowledge of the topology of the structure being segmented. We demonstrate our approach in four experiments: one on MNIST image denoising and digit recognition, one on left ventricular myocardium segmentation from magnetic resonance imaging data from the UK Biobank, one on the ACDC public challenge dataset and one on placenta segmentation from 3-D ultrasound. We find that embedding explicit prior knowledge in neural network segmentation tasks is most beneficial when the segmentation task is especially challenging and that it can be used in either a semi-supervised or post-processing context to extract a useful training gradient from images without pixelwise labels.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Convolutional Neural Networks ; Image Segmentation ; Loss Measurement ; Medical Imaging ; Network Topology ; Neural Networks ; Persistent Homology ; Segmentation ; Shape ; Topology ; Topology ; Training
Language english
Publication Year 2020
HGF-reported in Year 2020
ISSN (print) / ISBN 0162-8828
e-ISSN 1939-3539
Journal IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Reviewing status Peer reviewed
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-507100-001
DOI 10.1109/TPAMI.2020.3013679
Scopus ID 85101301735
Erfassungsdatum 2022-06-10
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