mlsanityData to 7 Oct 2026

EchoCP: An Echocardiography Dataset in Contrast Transthoracic Echocardiography for Patent Foramen Ovale Diagnosis

arXiv 2105.08267 ↗First posted 18 May 2021eess.IV cs.CVcited by 6

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Abstract (arXiv)

Patent foramen ovale (PFO) is a potential separation between the septum, primum and septum secundum located in the anterosuperior portion of the atrial septum. PFO is one of the main factors causing cryptogenic stroke which is the fifth leading cause of death in the United States. For PFO diagnosis, contrast transthoracic echocardiography (cTTE) is preferred as being a more robust method compared with others. However, the current PFO diagnosis through cTTE is extremely slow as it is proceeded manually by sonographers on echocardiography videos. Currently there is no publicly available dataset for this important topic in the community. In this paper, we present EchoCP, as the first echocardiography dataset in cTTE targeting PFO diagnosis. EchoCP consists of 30 patients with both rest and Valsalva maneuver videos which covers various PFO grades. We further establish an automated baseline method for PFO diagnosis based on the state-of-the-art cardiac chamber segmentation technique, which achieves 0.89 average mean Dice score, but only 0.60/0.67 mean accuracies for PFO diagnosis, leaving large room for improvement. We hope that the challenging EchoCP dataset can stimulate further research and lead to innovative and generic solutions that would have an impact in multiple domains. Our dataset is released.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 6 of its 21 references that are in the map. First posted on arXiv 18 May 2021.

Builds on

6 of 21 references.

References · 6

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  3. 2019Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiographycited by 243
  4. 2021ImageCHD: A 3D Computed Tomography Image Dataset for Classification of Congenital Heart Diseasecited by 16
  5. 2020ICA-UNet: ICA Inspired Statistical UNet for Real-time 3D Cardiac Cine MRI Segmentationcited by 9
  6. 2019MSU-Net: Multiscale Statistical U-Net for Real-time 3D Cardiac MRI Video Segmentationcited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 1; 2026: 3.

6 papers.