A Persistent Homology-Based Topological Loss for CNN-Based Multiclass Segmentation of CMR

Multi-class segmentation of cardiac magnetic resonance (CMR) images seeks a separation of data into anatomical components with known structure and configuration. The most popular CNN-based methods are optimised using pixel wise loss functions, ignorant of the spatially extended features that charact...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2023-01, Vol.42 (1), p.3-14
Main Authors: Byrne, Nick, Clough, James R., Valverde, Israel, Montana, Giovanni, King, Andrew P.
Format: Article
Language:English
Subjects:
CMR
CNN
Feature extraction
Heart
Heart - diagnostic imaging
Homology
Image processing
Image Processing, Computer-Assisted - methods
Image segmentation
Magnetic resonance
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Shape
Statistical analysis
Task analysis
Three-dimensional displays
Topology
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Summary:Multi-class segmentation of cardiac magnetic resonance (CMR) images seeks a separation of data into anatomical components with known structure and configuration. The most popular CNN-based methods are optimised using pixel wise loss functions, ignorant of the spatially extended features that characterise anatomy. Therefore, whilst sharing a high spatial overlap with the ground truth, inferred CNN-based segmentations can lack coherence, including spurious connected components, holes and voids. Such results are implausible, violating anticipated anatomical topology. In response, (single-class) persistent homology-based loss functions have been proposed to capture global anatomical features. Our work extends these approaches to the task of multi-class segmentation. Building an enriched topological description of all class labels and class label pairs, our loss functions make predictable and statistically significant improvements in segmentation topology using a CNN-based post-processing framework. We also present (and make available) a highly efficient implementation based on cubical complexes and parallel execution, enabling practical application within high resolution 3D data for the first time. We demonstrate our approach on 2D short axis and 3D whole heart CMR segmentation, advancing a detailed and faithful analysis of performance on two publicly available datasets.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2022.3203309