Compositional Segmentation of Cardiac Images Leveraging Metadata

Abbas Khan, Muhammad Asad, Martin Benning, Caroline Roney, Gregory Slabaugh; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 9471-9480

Cardiac image segmentation is essential for automated cardiac function assessment and monitoring of changes in cardiac structures over time. Inspired by coarse-to-fine approaches in image analysis we propose a novel multitask compositional segmentation approach that can simultaneously localize the heart in a cardiac image and perform part-based segmentation of different regions of interest. We demonstrate that this compositional approach achieves better results than direct segmentation of the anatomies. Further we propose a novel Cross-Modal Feature Integration (CMFI) module to leverage the metadata related to cardiac imaging collected during image acquisition. We perform experiments on two different modalities MRI and ultrasound using public datasets Multi-Disease Multi-View and Multi-Centre (M&Ms-2) and Multi-structure Ultrasound Segmentation (CAMUS) data to showcase the efficiency of the proposed compositional segmentation method and Cross-Modal Feature Integration module incorporating metadata within the proposed compositional segmentation network. The source code is available: https://github.com/kabbas570/CompSeg-MetaData.