A Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homology
By James R. Clough, Nicholas Byrne, Ilkay Oksuz, Veronika A. M. Zimmer, Julia Anne Schnabel, A. King. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019
309 citations recorded by the source corpus.
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.
Subfields: Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/451cdeb65780779607f50e5dd570913af8fb65d9
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
In Computer Vision and Pattern Recognition
- Bjoern H Menze
- Suprosanna Shit
- Ulrich Bauer
- Josien P. W. Pluim
- Max A. Viergever
- Marius Staring
- Stefan Klein
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- clDice - a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation (2020, background): “Another approach enables the predefinition of Betti numbers and enforces them on the training[8].”
- Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation (2022, unknown): “Experiments show that clinical-plausible segmentations are obtained.”