Incorporating Topological Knowledge Into Deep Learning Based Segmentation
(Funded by EPSRC grant EP/P001009/1, NIHR Doctoral Research Fellowship for Nick Byrne)
There are many cases in which the topology of a structure to be segmented is known a priori. For example, when segmenting the left ventricle of the heart from short-axis cine MR images, it is known that the correct segmentation will be a topological closed loop in two dimensions. However, the current state-of-the-art in deep learning based segmentation makes no use of this knowledge. In this work, we explicitly incorporate topological priors into a deep learning segmentation model for the first time, based on the concept of persistent homology. These priors require no additional ground truth segmentations and so the model is suitable for use in a semi-supervised setting. We found that by including the topological priors the number of topological errors was reduced whilst maintaining high overlap with the ground truth.
- J. R. Clough, I. Oksuz, N. Byrne, J. A. Schnabel, A. P. King, "Explicit Topological Priors for Deep-Learning Based Image Segmentation Using Persistent Homology", Proceedings IPMI, 2019. (Arxiv paper)
In related work, topological considerations were introduced into deep learning based 3D cardiac segmentation. In this research, it was shown that conventional data augmentation schemes that are widely employed to boost the performance of deep learning segmentation models result in violations of ground truth topology. Furthermore, conventional metrics of segmentation success such as the Dice coefficient are not sensitive to these violations. A straightforward topological correction scheme was proposed, based on the concept of cardiac contiguous topology, to correct the topology of ground truth augmentations. This resulted in fewer topological errors in the segmentations produced by the resulting model.
- N. Byrne, J. R. Clough, I. Valverde, G. Montana, A. P. King, "Topology-preserving Augmentation for CNN-based Segmentation of Congenital Heart Defects from 3D Paediatric CMR", Proceedings MICCAI PIPPI, 2019. (Arxiv paper)