# Recurrent Aggregation Learning for Multi-View Echocardiographic Sequences Segmentation

arXiv: https://arxiv.org/abs/1907.11292 · HTML: https://mlsanity.com/paper/1907.11292/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2019.

## Built on by (most cited first, 12 of 12)

- [From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare](https://mlsanity.com/paper/2409.09727/) (2024)
- [Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labels](https://mlsanity.com/paper/2202.07422/) (2022)
- [Reciprocal Landmark Detection and Tracking with Extremely Few Annotations](https://mlsanity.com/paper/2101.11224/) (2021)
- [Echo-SyncNet: Self-supervised Cardiac View Synchronization in Echocardiography](https://mlsanity.com/paper/2102.02287/) (2021)
- [Where to Begin? From Random to Foundation Model Instructed Initialization in Federated Learning for Medical Image Segmentation](https://mlsanity.com/paper/2311.15463/) (2023)
- [Bootstrap Representation Learning for Segmentation on Medical Volumes and Sequences](https://mlsanity.com/paper/2106.12153/) (2021)
- [Motion-enhanced Cardiac Anatomy Segmentation via an Insertable Temporal Attention Module](https://mlsanity.com/paper/2501.14929/) (2025)
- [Left Ventricle Contouring of Apical Three-Chamber Views on 2D Echocardiography](https://mlsanity.com/paper/2207.06330/) (2022)
- [SimLVSeg: Simplifying Left Ventricular Segmentation in 2D+Time Echocardiograms with Self- and Weakly-Supervised Learning](https://mlsanity.com/paper/2310.00454/) (2023)
- [EchoDFKD: Data-Free Knowledge Distillation for Cardiac Ultrasound Segmentation using Synthetic Data](https://mlsanity.com/paper/2409.07566/) (2024)
- [Fully Automated Machine Learning Pipeline for Echocardiogram Segmentation](https://mlsanity.com/paper/2107.08440/) (2021)
- [A DyL-Unet framework based on dynamic learning for Temporally Consistent Echocardiographic Segmentation](https://mlsanity.com/paper/2509.19052/) (2025)

## Abstract (arXiv)

Multi-view echocardiographic sequences segmentation is crucial for clinical diagnosis. However, this task is challenging due to limited labeled data, huge noise, and large gaps across views. Here we propose a recurrent aggregation learning method to tackle this challenging task. By pyramid ConvBlocks, multi-level and multi-scale features are extracted efficiently. Hierarchical ConvLSTMs next fuse these features and capture spatial-temporal information in multi-level and multi-scale space. We further introduce a double-branch aggregation mechanism for segmentation and classification which are mutually promoted by deep aggregation of multi-level and multi-scale features. The segmentation branch provides information to guide the classification while the classification branch affords multi-view regularization to refine segmentations and further lessen gaps across views. Our method is built as an end-to-end framework for segmentation and classification. Adequate experiments on our multi-view dataset (9000 labeled images) and the CAMUS dataset (1800 labeled images) corroborate that our method achieves not only superior segmentation and classification accuracy but also prominent temporal stability.