Temporal Representation Learning for Real-Time Ultrasound Analysis

2025-09-01

AI Summary

Existing ultrasound video analysis methods typically treat frames as independent samples, neglecting the temporal continuity of cardiac motion and thus exhibiting limited capability in modeling sequential dynamics. To address this, we propose a temporal-aware self-supervised representation learning framework that innovatively integrates a time-consistent masking strategy with contrastive learning to enforce physiological coherence in inter-frame motion modeling. Additionally, we introduce a video-level temporal modeling module to enhance representation learning for periodic cardiac motion. Evaluated on the EchoNet-Dynamic dataset, our method achieves a significant reduction in ejection fraction prediction error (18.7% decrease in MAE), demonstrating the critical role of temporal structural priors in ultrasound representation learning. This work establishes a novel, interpretable, and annotation-efficient paradigm for real-time, accurate cardiac functional assessment.

Technology Category

• Machine Learning: Representation Learning
• Computer Vision: Motion & Tracking
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Ultrasound (US) imaging is a critical tool in medical diagnostics, offering real-time visualization of physiological processes. One of its major advantages is its ability to capture temporal dynamics, which is essential for assessing motion patterns in applications such as cardiac monitoring, fetal development, and vascular imaging. Despite its importance, current deep learning models often overlook the temporal continuity of ultrasound sequences, analyzing frames independently and missing key temporal dependencies. To address this gap, we propose a method for learning effective temporal representations from ultrasound videos, with a focus on echocardiography-based ejection fraction (EF) estimation. EF prediction serves as an ideal case study to demonstrate the necessity of temporal learning, as it requires capturing the rhythmic contraction and relaxation of the heart. Our approach leverages temporally consistent masking and contrastive learning to enforce temporal coherence across video frames, enhancing the model's ability to represent motion patterns. Evaluated on the EchoNet-Dynamic dataset, our method achieves a substantial improvement in EF prediction accuracy, highlighting the importance of temporally-aware representation learning for real-time ultrasound analysis.

Problem

• Learning temporal representations from ultrasound videos
• Addressing overlooked temporal continuity in ultrasound sequences
• Improving ejection fraction estimation accuracy in echocardiography

Innovation

• Temporally consistent masking for frame coherence
• Contrastive learning to enhance motion patterns
• Real-time ultrasound video representation learning