Compositional Segmentation of Cardiac Images Leveraging Metadata

Abbas Khan, Muhammad Asad, Martin Benning, Caroline Roney, Gregory Slabaugh

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 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