EchoCP_dataset

@article{,
title= {EchoCP_dataset},
keywords= {deep learning, ultrasound, Echocardiography, Heart Conditions, Image Segmentation},
author= {},
abstract= {A dataset of contrast transthoracic echocardiography, EchoCP, for patent foramen ovale diagnosis is published.

We present EchoCP, the first dataset for cTTE based PFO diagnosis. EchoCP contains both VM and rest echocardiography videos captured from 30 patients. Data annotation including diagnosis annotation and segmentation annotation are performed by four experienced cardiovascular sonographers. As there are more than a thousand images in each patient's video, sparse labeling (only select representative frames) of the segmentation is adopted.

EchoCP contains cTTE videos from 30 patients. For each patient, two videos corresponding to the rest and VM state of the patients are captured. Note that in the rest state, patients just relax and breathe normally. While in the VM, patients need to close their mouths, pinch their noses shut while expelling air out as if blowing up a balloon. The video is captured in the apical-4-chamber view and contains at least ten cardiac cycles. For the VM state, the action is performed three to five times during acquisition, and we selected the most representative one.

If you used our dataset, please consider to cite our paper in MICCAI 2021, Tianchen Wang, Zhihe Li, Shanshan Bi, Meiping Huang, Jiawei Zhang, Jian Zhuang, Yiyu Shi, Hongwen Fei, Xiaowei Xu, "ImageCHD: A 3D Computed Tomography Image Dataset for Classification of Congenital Heart Disease," in Proc. of Medical Image Computing and Computer Assisted Interventions (MICCAI), Online, 2021.
https://arxiv.org/abs/2101.10799

HIGHLIGHT 20231101: We have deployed the dataset on Kaggle!

Please send emails to me xiao.wei.xu@foxmail.com if you have any questions about the dataset and the benchmark.
},
terms= {},
license= {https://www.apache.org/licenses/LICENSE-2.0},
superseded= {},
url= {https://www.kaggle.com/datasets/xiaoweixumedicalai/echocp}
}