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arXiv · 2508.12023

WiseLVAM: A Novel Framework For Left Ventricle Automatic Measurements

Durgesh Kumar Singh·Qing Cao·Sarina Thomas·Ahcène Boubekki·Robert Jenssen·Michael Kampffmeyer
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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 \textit{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. \textit{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. The source code is publicly available at https://github.com/SFI-Visual-Intelligence/wiselvam.git.

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Durgesh Kumar Singh, Qing Cao, Sarina Thomas, Ahcène Boubekki, Robert Jenssen, Michael Kampffmeyer. 2025-09-15. WiseLVAM: A Novel Framework For Left Ventricle Automatic Measurements. https://doi.org/10.1007/978-3-032-06329-8_21

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