Crop and Couple: cardiac image segmentation using interlinked specialist networks
This work addresses automated cardiac image segmentation for medical diagnosis, presenting an incremental improvement through a novel hybrid approach.
The paper tackles cardiac image segmentation for cardiovascular disease diagnosis by proposing a method that uses specialist networks for individual anatomies, coupled via an attention mechanism, achieving improved segmentation accuracy with concrete performance gains reported.
Diagnosis of cardiovascular disease using automated methods often relies on the critical task of cardiac image segmentation. We propose a novel strategy that performs segmentation using specialist networks that focus on a single anatomy (left ventricle, right ventricle, or myocardium). Given an input long-axis cardiac MR image, our method performs a ternary segmentation in the first stage to identify these anatomical regions, followed by cropping the original image to focus subsequent processing on the anatomical regions. The specialist networks are coupled through an attention mechanism that performs cross-attention to interlink features from different anatomies, serving as a soft relative shape prior. Central to our approach is an additive attention block (E-2A block), which is used throughout our architecture thanks to its efficiency.