EnLVAM: Enhanced Left Ventricle Linear Measurements Utilizing Anatomical Motion Mode

2025-06-27

AI Summary

Manual landmark annotation for linear measurements of the left ventricle in B-mode parasternal long-axis (PLAX) views is time-consuming and error-prone; existing deep learning methods suffer from severe landmark misalignment. Method: We propose a semi-automatic measurement framework integrating anatomically informed M-mode imaging and geometric constraints. First, anatomical M-mode images are synthesized from B-mode video to enhance spatiotemporal consistency of endocardial motion. Second, key anatomical landmarks are detected in the M-mode domain, and a collinearity constraint is enforced via line-based regularization to correct misalignments. Finally, optimized landmarks are projected back onto the B-mode image for quantitative measurement. The framework is architecture-agnostic and supports interactive refinement. Results: Experiments demonstrate statistically significant reduction in measurement error versus conventional B-mode approaches (p < 0.01), with strong generalizability and clinical applicability.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Security and Privacy: Large-scale security measurements
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

Linear measurements of the left ventricle (LV) in the Parasternal Long Axis (PLAX) view using B-mode echocardiography are crucial for cardiac assessment. These involve placing 4-6 landmarks along a virtual scanline (SL) perpendicular to the LV axis near the mitral valve tips. Manual placement is time-consuming and error-prone, while existing deep learning methods often misalign landmarks, causing inaccurate measurements. We propose a novel framework that enhances LV measurement accuracy by enforcing straight-line constraints. A landmark detector is trained on Anatomical M-Mode (AMM) images, computed in real time from B-mode videos, then transformed back to B-mode space. This approach addresses misalignment and reduces measurement errors. Experiments show improved accuracy over standard B-mode methods, and the framework generalizes well across network architectures. Our semi-automatic design includes a human-in-the-loop step where the user only places the SL, simplifying interaction while preserving alignment flexibility and clinical relevance.

Problem

• Improving left ventricle measurement accuracy in echocardiography
• Reducing manual landmark placement errors in cardiac assessment
• Enhancing deep learning alignment for precise linear measurements

Innovation

• Enforcing straight-line constraints for LV measurements
• Using AMM images for landmark detection
• Semi-automatic design with human-in-the-loop