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
- 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
- Physics-Informed Deep Learning for False Ventricular Tachycardia Alarm Reduction in the ICU [44.313512671851306]
False ventricular tachycardia (VT) alarms are a leading contributor to alarm fatigue in intensive care units.<n>We propose a deep learning framework combining a 1D SE-ResNet with ICU-realistic data augmentations and a physics-informed auxiliary reconstruction task.
arXiv Detail & Related papers (2026-09-08T16:33:07Z) - ECG-LLM: Foundation Model for ECG-Based Cardiac Reasoning [50.82313761980655]
ECG-LLM is an ECG-conditioned large language model trained across four cohorts 679,112 ECG studies from 186,409 patients.<n>It is trained on clinically structured question-answer pairs derived from ECG signals, clinical context, CMR, and ECHO.<n>It successfully recovers conventional ECG measurements, such as heart rate, and strongly predicts complex CMR-derived phenotypes.
arXiv Detail & Related papers (2026-07-15T11:06:35Z) - Learning Cardiac Latent Representations in Vectorcardiogram Space [54.12639543496968]
Existing methods operate almost exclusively in the observable ECG signal space.<n>We propose learning a unified latent representation of cardiac electrical activity directly in the VCG space.<n>We introduce LVCG, the first general self-supervised representation learning framework designed to operate in this physically grounded latent space.
arXiv Detail & Related papers (2026-05-29T12:48:12Z) - Echo2ECG: Enhancing ECG Representations with Cardiac Morphology from Multi-View Echos [53.368813255127115]
Existing self-supervised methods suffer from a representational mismatch by aligning ECGs to single-view Echos.<n>We propose Echo2ECG, a multimodal self-supervised learning framework that enriches ECG representations with the heart's morphological structure captured in multi-view Echos.<n>We evaluate Echo2ECG as an ECG feature extractor on two clinically relevant tasks.
arXiv Detail & Related papers (2026-03-09T15:39:57Z) - 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.