FrameONE: Hierarchical Motion Modeling for Universal Multi-View Echocardiographic Keyframe Detection
2026-07-01 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors developed FrameONE, a method to find important heart ultrasound frames called end-systole and end-diastole across different types of heart images. Traditional methods often work only for one image view and need extra help or complex designs. FrameONE uses a two-level approach to learn heart motion patterns both within each view and across different views, making it flexible and more accurate. They tested their method on a large dataset and showed it works very well for many views without needing extra annotations.
echocardiographyend-systoleend-diastolekeyframe detectionmulti-view modelingmotion representationmulti-task learningcardiac motioncomputer visiondeep learning
Authors
Rusi Chen, Yuhao Huang, Hongyuan Zhang, Chao Tian, Shunan Ji, Yuhan Zhang, Dong Ni
Abstract
Accurate detection of end-systole (ES) and end-diastole (ED) frames is fundamental to echocardiographic assessment. Existing methods are typically developed in a view-specific manner, depend on auxiliary annotations or intensive visual modeling, which limits their generalizability. In multi-view modeling, keyframe detection is driven by shared cardiac motion, yet large appearance differences and motion patterns make unified modeling challenging. To address these issues, we propose FrameONE, a unified end-to-end framework for multi-view echocardiographic keyframe detection. FrameONE introduces a Hierarchical Motion Modeling strategy: an intra-view multi-task learning reduces appearance bias and promotes motion-focused representations within each view; an inter-view general motion learning module further separates view-agnostic dynamics from view-specific patterns, enabling shared yet flexible motion representation learning across views. Extensive experiments on 25,872 videos spanning four standard views demonstrate that FrameONE achieves state-of-the-art keyframe detection accuracy with strong cross-view generalization. Code is available at https://github.com/szuboy/FrameONE.