Khan, A; Asad, M; Benning, M; Roney, C; Slabaugh, G; (2025) Compositional Segmentation of Cardiac Images Leveraging Metadata. In: Proceedings 2025 IEEE Winter Conference on Applications of Computer Vision Wacv 2025. (pp. pp. 9489-9498). IEEE: Tucson, AZ, USA. (In press).

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Abstract

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 meta-data 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.

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