# Temporally Consistent Mitral Annulus Measurements from Sparse Annotations in Echocardiographic Videos

arXiv: https://arxiv.org/abs/2503.12087 · HTML: https://mlsanity.com/paper/2503.12087/

## 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.

## Builds on (most cited first, 5 of 5)

- [Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography](https://mlsanity.com/paper/1908.06948/) (2019)
- [High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy with Cardiovascular Deep Learning](https://mlsanity.com/paper/2106.12511/) (2021)
- [Deep Learning-Based Regression and Classification for Automatic Landmark Localization in Medical Images](https://mlsanity.com/paper/2007.05295/) (2020)
- [Automated Left Ventricle Dimension Measurement in 2D Cardiac Ultrasound via an Anatomically Meaningful CNN Approach](https://mlsanity.com/paper/1911.02448/) (2019)
- [Reciprocal Landmark Detection and Tracking with Extremely Few Annotations](https://mlsanity.com/paper/2101.11224/) (2021)

## 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.