# Reciprocal Landmark Detection and Tracking with Extremely Few Annotations

arXiv: https://arxiv.org/abs/2101.11224 · HTML: https://mlsanity.com/paper/2101.11224/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 27 references that are in the map.
- First posted on arXiv 27 Jan 2021.

## Builds on (most cited first, 14 of 14)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation](https://mlsanity.com/paper/1406.2984/) (2014)
- [Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation](https://mlsanity.com/paper/1705.08302/) (2017)
- [Flowing ConvNets for Human Pose Estimation in Videos](https://mlsanity.com/paper/1506.02897/) (2015)
- [Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences](https://mlsanity.com/paper/1806.04066/) (2018)
- [Weakly Supervised Medical Diagnosis and Localization from Multiple Resolutions](https://mlsanity.com/paper/1803.07703/) (2018)
- [A Combined Deep-Learning and Deformable-Model Approach to Fully Automatic Segmentation of the Left Ventricle in Cardiac MRI](https://mlsanity.com/paper/1512.07951/) (2015)
- [Recurrent Aggregation Learning for Multi-View Echocardiographic Sequences Segmentation](https://mlsanity.com/paper/1907.11292/) (2019)
- [Automated Left Ventricle Dimension Measurement in 2D Cardiac Ultrasound via an Anatomically Meaningful CNN Approach](https://mlsanity.com/paper/1911.02448/) (2019)
- [Left Ventricle Segmentation via Optical-Flow-Net from Short-axis Cine MRI: Preserving the Temporal Coherence of Cardiac Motion](https://mlsanity.com/paper/1810.08753/) (2018)
- [Automated segmentation on the entire cardiac cycle using a deep learning work-flow](https://mlsanity.com/paper/1809.01015/) (2018)
- [TAN: Temporal Affine Network for Real-Time Left Ventricle Anatomical Structure Analysis Based on 2D Ultrasound Videos](https://mlsanity.com/paper/1904.00631/) (2019)

## Built on by (most cited first, 4 of 4)

- [EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on Echocardiograms](https://mlsanity.com/paper/2307.12229/) (2023)
- [EnLVAM: Enhanced Left Ventricle Linear Measurements Utilizing Anatomical Motion Mode](https://mlsanity.com/paper/2506.22063/) (2025)
- [WiseLVAM: A Novel Framework For Left Ventricle Automatic Measurements](https://mlsanity.com/paper/2508.12023/) (2025)
- [Temporally Consistent Mitral Annulus Measurements from Sparse Annotations in Echocardiographic Videos](https://mlsanity.com/paper/2503.12087/) (2025)

## Abstract (arXiv)

Localization of anatomical landmarks to perform two-dimensional measurements in echocardiography is part of routine clinical workflow in cardiac disease diagnosis. Automatic localization of those landmarks is highly desirable to improve workflow and reduce interobserver variability. Training a machine learning framework to perform such localization is hindered given the sparse nature of gold standard labels; only few percent of cardiac cine series frames are normally manually labeled for clinical use. In this paper, we propose a new end-to-end reciprocal detection and tracking model that is specifically designed to handle the sparse nature of echocardiography labels. The model is trained using few annotated frames across the entire cardiac cine sequence to generate consistent detection and tracking of landmarks, and an adversarial training for the model is proposed to take advantage of these annotated frames. The superiority of the proposed reciprocal model is demonstrated using a series of experiments.