ArticleStatistical atlases and computational models of the heart. STACOM (Workshop)2020
A Persistent Homology-Based Topological Loss Function for Multi-class CNN Segmentation of Cardiac MRI.
Article in Statistical atlases and computational models of the heart. STACOM (Workshop), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Modality-AGnostic image Cascade (MAGIC) for multi-modality cardiac substructure segmentation.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2026Article
- Topological data analysis and machine learning for COVID-19 detection in CT scan lung images.BMC biomedical engineering · 2025Article
- Anatomically plausible segmentations: Explicitly preserving topology through prior deformations.Medical image analysis · 2024Article
- Projected pooling loss for red nucleus segmentation with soft topology constraints.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Machine learning techniques on homological persistence features for prostate cancer diagnosis.BMC bioinformatics · 2022Article
- TopoFit: Rapid Reconstruction of Topologically-Correct Cortical Surfaces.Proceedings of machine learning research · 2022Article
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4 authors.
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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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