mlsanityData to 14 Oct 2026

Temporally Consistent Mitral Annulus Measurements from Sparse Annotations in Echocardiographic Videos

arXiv 2503.12087 ↗First posted 15 Mar 2025cs.CVcited by 0

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Abstract (arXiv)

This work presents a novel approach to achieving temporally consistent mitral annulus landmark localization in echocardiography videos using sparse annotations. Our method introduces a self-supervised loss term that enforces temporal consistency between neighboring frames, which smooths the position of landmarks and enhances measurement accuracy over time. Additionally, we incorporate realistic field-of-view augmentations to improve the recognition of missing anatomical landmarks. We evaluate our approach on both a public and private dataset, and demonstrate significant improvements in Mitral Annular Plane Systolic Excursion (MAPSE) calculations and overall landmark tracking stability. The method achieves a mean absolute MAPSE error of 1.81 ± 0.14 mm, an annulus size error of 2.46 ± 0.31 mm, and a landmark localization error of 2.48 ± 0.07 mm. Finally, it achieves a 0.99 ROC-AUC for recognition of missing landmarks.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 12 references that are in the map. First posted on arXiv 15 Mar 2025.

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5 of 12 references.

References · 5

  1. 2019Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiographycited by 244
  2. 2021High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy with Cardiovascular Deep Learningcited by 37
  3. 2020Deep Learning-Based Regression and Classification for Automatic Landmark Localization in Medical Imagescited by 22
  4. 2019Automated Left Ventricle Dimension Measurement in 2D Cardiac Ultrasound via an Anatomically Meaningful CNN Approachcited by 9
  5. 2021Reciprocal Landmark Detection and Tracking with Extremely Few Annotationscited by 4

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