Towards more accurate and label-efficient Left Ventricle Automatic Measurements

Abstract

<p>Accurate left ventricular (LV) linear measurements are critical for cardiac function assessment in echocardiography. However, manual measurements are time-consuming and prone to variability due to anatomical complexity and operator dependency. While deep learning (DL) models have automated landmark detection, existing fully automated B-mode approaches often produce inaccurate predictions caused by shifted landmarks and lack the flexibility to handle clinically challenging cases such as septal bulge or mid-ventricular measurements. This thesis addresses these challenges by developing methods to improve automation, accuracy, and generalization in echocardiographic LV linear measurements. <p>The first contribution presents a semi-automatic framework, Enhanced LVAM (EnLVAM), that leverages Anatomical Motion Mode (AMM) imaging to constrain landmark predictions along a user-defined virtual scanline. By aligning predictions with clinically relevant orientations, EnLVAM reduces variability and overcomes the limitations of B-mode models, particularly in complex anatomical scenarios. The semi-automatic design further allows human feedback, providing flexibility and robustness, and demonstrates improved measurement accuracy over fully automated baselines. <p>Building on this, the second contribution proposes WiseLVAM, which advances the framework toward full automation by eliminating the need for manual scanline selection. WiseLVAM learns to predict the optimal scanline position directly from B-mode images using weak supervision generated by the AMM-based approach. By estimating the LV contour and long axis, WiseLVAM performs shape-aware scanline placement aligned with clinical guidelines. The model then utilizes the trained AMM-based detector to execute precise LV linear measurements, offering a fully automated and clinically feasible solution. <p>Finally, the thesis explores learning under limited supervision by proposing a clustering-based regularization framework (SuperCM) designed for semi-supervised learning (SSL) and unsupervised domain adaptation (UDA). SuperCM explicitly enforces clustering assumptions during training, promoting compact and class-consistent feature representations. This approach improves model generalization when labeled data is scarce—a common challenge in medical imaging—and demonstrates effectiveness on standard SSL and UDA benchmarks. <p>Overall, this thesis presents a comprehensive framework that advances echocardiography analysis by combining anatomical constraints and weak supervision to improve the accuracy and reliability of LV linear measurements. Additionally, the thesis provides a detailed discussion highlighting the contributions within the broader research context of echocardiography analysis and outlines future research directions for developing robust and clinically deployable solutions.