Orientation-Robust Latent Motion Trajectory Learning for Annotation-free Cardiac Phase Detection in Fetal Echocardiography
🤖 AI Summary
Automated detection of end-diastolic (ED) and end-systolic (ES) frames in fetal four-chamber-view echocardiography videos remains highly challenging due to the absence of manual annotations and substantial variability in cardiac orientation. This work proposes ORBIT, a novel framework that, for the first time, enables annotation-free and orientation-agnostic ED/ES localization. ORBIT leverages self-supervised image registration to learn latent motion trajectories of cardiac deformation and precisely identifies critical phase transitions in the cardiac cycle by detecting inflection points along these trajectories. The method demonstrates markedly improved generalization across diverse cardiac poses, achieving mean ED/ES frame errors of 1.9/1.6 frames in normal fetuses and 2.4/2.1 frames in those with congenital heart disease—significantly outperforming existing unsupervised approaches.
Application Category
📝 Abstract
Fetal echocardiography is essential for detecting congenital heart disease (CHD), facilitating pregnancy management, optimized delivery planning, and timely postnatal interventions. Among standard imaging planes, the four-chamber (4CH) view provides comprehensive information for CHD diagnosis, where clinicians carefully inspect the end-diastolic (ED) and end-systolic (ES) phases to evaluate cardiac structure and motion. Automated detection of these cardiac phases is thus a critical component toward fully automated CHD analysis. Yet, in the absence of fetal electrocardiography (ECG), manual identification of ED and ES frames remains a labor-intensive bottleneck. We present ORBIT (Orientation-Robust Beat Inference from Trajectories), a self-supervised framework that identifies cardiac phases without manual annotations under various fetal heart orientation. ORBIT employs registration as self-supervision task and learns a latent motion trajectory of cardiac deformation, whose turning points capture transitions between cardiac relaxation and contraction, enabling accurate and orientation-robust localization of ED and ES frames across diverse fetal positions. Trained exclusively on normal fetal echocardiography videos, ORBIT achieves consistent performance on both normal (MAE = 1.9 frames for ED and 1.6 for ES) and CHD cases (MAE = 2.4 frames for ED and 2.1 for ES), outperforming existing annotation-free approaches constrained by fixed orientation assumptions. These results highlight the potential of ORBIT to facilitate robust cardiac phase detection directly from 4CH fetal echocardiography.
Problem
Research questions and friction points this paper is trying to address.
fetal echocardiography
cardiac phase detection
annotation-free
orientation robustness
congenital heart disease
Innovation
Methods, ideas, or system contributions that make the work stand out.
self-supervised learning
latent motion trajectory
orientation-robust
cardiac phase detection
fetal echocardiography
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