GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation

TL;DR

GraphEcho introduces a graph-based approach leveraging cardiac structure and heartbeat cycle consistency to improve unsupervised domain adaptation in echocardiogram video segmentation, outperforming existing methods.

Contribution

The paper presents a novel GraphEcho method with modules for cross-domain graph matching and cycle consistency, tailored for echocardiogram videos, and introduces a new dataset for this task.

Findings

GraphEcho outperforms state-of-the-art UDA segmentation methods.

The SCGM and TCC modules effectively align features across domains.

The new CardiacUDA dataset facilitates future research in this area.

Abstract

Echocardiogram video segmentation plays an important role in cardiac disease diagnosis. This paper studies the unsupervised domain adaption (UDA) for echocardiogram video segmentation, where the goal is to generalize the model trained on the source domain to other unlabelled target domains. Existing UDA segmentation methods are not suitable for this task because they do not model local information and the cyclical consistency of heartbeat. In this paper, we introduce a newly collected CardiacUDA dataset and a novel GraphEcho method for cardiac structure segmentation. Our GraphEcho comprises two innovative modules, the Spatial-wise Cross-domain Graph Matching (SCGM) and the Temporal Cycle Consistency (TCC) module, which utilize prior knowledge of echocardiogram videos, i.e., consistent cardiac structure across patients and centers and the heartbeat cyclical consistency, respectively. These two modules can better align global and local features from source and target domains, improving UDA segmentation results. Experimental results showed that our GraphEcho outperforms existing state-of-the-art UDA segmentation methods. Our collected dataset and code will be publicly released upon acceptance. This work will lay a new and solid cornerstone for cardiac structure segmentation from echocardiogram videos. Code and dataset are available at: https://github.com/xmed-lab/GraphEcho