Orientation-Robust Latent Motion Trajectory Learning for Annotation-free Cardiac Phase Detection in Fetal Echocardiography

• URL: http://arxiv.org/abs/2602.06761v1
• Date: Fri, 06 Feb 2026 15:13:53 GMT
• Title: Orientation-Robust Latent Motion Trajectory Learning for Annotation-free Cardiac Phase Detection in Fetal Echocardiography
• Authors: Yingyu Yang, Qianye Yang, Can Peng, Elena D'Alberti, Olga Patey, Aris T. Papageorghiou, J. Alison Noble,
• Abstract summary: ORBIT (Orientation-Robust Beat Inference from Trajectories) is a self-supervised framework that identifies cardiac phases without manual annotations under various fetal heart orientation.<n> trained exclusively on normal fetal echocardiography videos, ORBIT achieves consistent performance on both normal and congenital heart disease cases.
• Score: 6.991329014964486
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

Related papers

• Tracing the Heart's Pathways: ECG Representation Learning from a Cardiac Conduction Perspective [23.944422144809234]
  Recent strides in electrocardiogram self-supervised learning (eSSL) have brightened prospects for enhancing representation learning.<n>We propose CLEAR-HUG, a two-stage framework designed to capture subtle variations in cardiac conduction across leads.<n> Experimental results across six tasks show a 6.84% improvement, validating the effectiveness of CLEAR-HUG.
  arXiv  Detail & Related papers  (2025-12-30T05:46:48Z)
• Latent Representations of Intracardiac Electrograms for Atrial Fibrillation Driver Detection [37.72464514643607]
  This study proposes a deep learning framework using convolutional autoencoders for unsupervised feature extraction.<n> latent representations of atrial electrical activity enable the characterization and automation of EGM analysis.<n>The proposed method can operate in real-time and enables integration into clinical electroanatomical mapping systems to assist in identifying arrhythmogenic regions during ablation procedures.
  arXiv  Detail & Related papers  (2025-07-24T09:40:24Z)
• Latent Motion Profiling for Annotation-free Cardiac Phase Detection in Adult and Fetal Echocardiography Videos [4.306805601880343]
  We present an unsupervised framework for end-diastole (ED) and end-systole (ES) detection through self-supervised learning.<n>Our method eliminates need for manual annotations, including ED ES indices, segmentation, or volumetric measurements.<n>It achieves mean absolute error (MAE) of 3 frames (5Phase ms) for ED and 2 frames (38.8 ms) for ES detection, matching state-of-the-art supervised methods.
  arXiv  Detail & Related papers  (2025-07-07T16:10:46Z)
• Global and Local Contrastive Learning for Joint Representations from Cardiac MRI and ECG [40.407824759778784]
  PTACL (Patient and Temporal Alignment Contrastive Learning) is a multimodal contrastive learning framework that enhances ECG representations by integrating-temporal information from CMR.<n>We evaluate PTACL on paired ECG-CMR data from 27,951 subjects in the UK Biobank.<n>Our results highlight the potential of PTACL to enhance non-invasive cardiac diagnostics using ECG.
  arXiv  Detail & Related papers  (2025-06-24T17:19:39Z)
• Heartcare Suite: Multi-dimensional Understanding of ECG with Raw Multi-lead Signal Modeling [50.58126509704037]
  Heartcare Suite is a framework for fine-grained electrocardiogram (ECG) understanding.<n>Heartcare-220K is a high-quality, structured, and comprehensive multimodal ECG dataset.<n>Heartcare-Bench is a benchmark to guide the optimization of Medical Multimodal Large Language Models (Med-MLLMs) in ECG scenarios.
  arXiv  Detail & Related papers  (2025-06-06T07:56:41Z)
• Self-supervised inter-intra period-aware ECG representation learning for detecting atrial fibrillation [41.82319894067087]
  We propose an inter-intra period-aware ECG representation learning approach. Considering ECGs of atrial fibrillation patients exhibit the irregularity in RR intervals and the absence of P-waves, we develop specific pre-training tasks for interperiod and intraperiod representations. Our approach demonstrates remarkable AUC performances on the BTCH dataset, textiti.e., 0.953/0.996 for paroxysmal/persistent atrial fibrillation detection.
  arXiv  Detail & Related papers  (2024-10-08T10:03:52Z)
• Advanced Neural Network Architecture for Enhanced Multi-Lead ECG Arrhythmia Detection through Optimized Feature Extraction [0.0]
  Arrhythmia, characterized by irregular heart rhythms, presents formidable diagnostic challenges. This study introduces an innovative approach utilizing deep learning techniques to address the complexities of arrhythmia classification.
  arXiv  Detail & Related papers  (2024-04-13T19:56:15Z)
• Deciphering Heartbeat Signatures: A Vision Transformer Approach to Explainable Atrial Fibrillation Detection from ECG Signals [4.056982620027252]
  We develop a vision transformer approach to identify atrial fibrillation based on single-lead ECG data. A residual network (ResNet) approach is also developed for comparison with the vision transformer approach.
  arXiv  Detail & Related papers  (2024-02-12T11:04:08Z)
• Semantic-aware Temporal Channel-wise Attention for Cardiac Function Assessment [69.02116920364311]
  Existing video-based methods do not pay much attention to the left ventricular region, nor the left ventricular changes caused by motion. We propose a semi-supervised auxiliary learning paradigm with a left ventricular segmentation task, which contributes to the representation learning for the left ventricular region. Our approach achieves state-of-the-art performance on the Stanford dataset with an improvement of 0.22 MAE, 0.26 RMSE, and 1.9% $R2$.
  arXiv  Detail & Related papers  (2023-10-09T05:57:01Z)
• Noise-Resilient Automatic Interpretation of Holter ECG Recordings [67.59562181136491]
  We present a three-stage process for analysing Holter recordings with robustness to noisy signal. First stage is a segmentation neural network (NN) with gradientdecoder architecture which detects positions of heartbeats. Second stage is a classification NN which will classify heartbeats as wide or narrow. Third stage is a boosting decision trees (GBDT) on top of NN features that incorporates patient-wise features.
  arXiv  Detail & Related papers  (2020-11-17T16:15:49Z)
• Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation [3.3426603061273994]
  We propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes. We achieved a state-of-the-art average classification accuracy of 99.11%, which is better than those of recently reported results in classifying similar types of arrhythmias.
  arXiv  Detail & Related papers  (2020-05-14T12:11:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.