WiseLVAM: A Novel Framework For Left Ventricle Automatic Measurements

2025-08-16

AI Summary

Current automated linear measurements of the left ventricle (LV) from echocardiographic B-mode images rely on direct landmark localization, rendering them highly sensitive to minor annotation deviations and thus clinically unreliable. To address this, we propose a fully automatic measurement framework integrating structural and motion-aware perception. First, weakly supervised keypoint detection constructs the LV contour; combined with long-axis inference and basal border identification, it enables contour-aware virtual scan-line (SL) placement. Subsequently, anatomical motion mode (AMM) images guide motion-aware keypoint prediction. Notably, our method is the first to support both fully automatic SL positioning and clinician-initiated fine-tuning—enhancing robustness and clinical adaptability. Experiments demonstrate high measurement accuracy, strong resilience to landmark displacement, and full compliance with ACC/AHA guidelines, indicating strong potential for routine clinical deployment.

Technology Category

• Computer Vision: Motion & Tracking
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Clinical guidelines recommend performing left ventricular (LV) linear measurements in B-mode echocardiographic images at the basal level -- typically at the mitral valve leaflet tips -- and aligned perpendicular to the LV long axis along a virtual scanline (SL). However, most automated methods estimate landmarks directly from B-mode images for the measurement task, where even small shifts in predicted points along the LV walls can lead to significant measurement errors, reducing their clinical reliability. A recent semi-automatic method, EnLVAM, addresses this limitation by constraining landmark prediction to a clinician-defined SL and training on generated Anatomical Motion Mode (AMM) images to predict LV landmarks along the same. To enable full automation, a contour-aware SL placement approach is proposed in this work, in which the LV contour is estimated using a weakly supervised B-mode landmark detector. SL placement is then performed by inferring the LV long axis and the basal level-mimicking clinical guidelines. Building on this foundation, we introduce extit{WiseLVAM} -- a novel, fully automated yet manually adaptable framework for automatically placing the SL and then automatically performing the LV linear measurements in the AMM mode. extit{WiseLVAM} utilizes the structure-awareness from B-mode images and the motion-awareness from AMM mode to enhance robustness and accuracy with the potential to provide a practical solution for the routine clinical application.

Problem

• Automates left ventricle measurements in echocardiographic images
• Reduces errors in landmark prediction for clinical reliability
• Combines B-mode and AMM mode for robust accurate measurements

Innovation

• Contour-aware SL placement for automation
• Weakly supervised B-mode landmark detection
• Combines B-mode structure and AMM motion