ImageCHD先天性心脏病CT数据集
ImageCHD先天性心脏病CT数据集收录先天性心脏病三维CT影像、心脏结构标签和疾病类型信息，数据模态为图像，适合艾森曼格综合征相关病例识别、诊断建模、分割检测、风险评估或临床研究复现。
基本信息
资源简介
ImageCHD 数据集包含先天性心脏病三维CT影像、心脏结构标签和疾病类型信息，可围绕艾森曼格综合征（Eisenmenger syndrome）开展病例筛选、特征提取、诊断分类、分割检测、风险评估或预后分析。
https://github.com/XiaoweiXu/ImageCHD-A-3D-Computed-Tomography-Image-Dataset-for-Classification-of-Congenital-Heart-Disease
arXiv 论文 →下载信息
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使用方式
数据集获取
git clone https://github.com/XiaoweiXu/ImageCHD-A-3D-Computed-Tomography-Image-Dataset-for-Classification-of-Congenital-Heart-Disease.git
curl -L -o repo.zip https://github.com/XiaoweiXu/ImageCHD-A-3D-Computed-Tomography-Image-Dataset-for-Classification-of-Congenital-Heart-Disease/archive/refs/heads/master.zip
unzip repo.zip
源站 README 摘录（使用方式）
ImageCHD-A-3D-Computed-TomographyImage-Dataset-for-Classification-of-Congenital-Heart-Disease
A dataset of A 3D Computed Tomography (CT) image dataset, ImageChD, for classification of Congenital Heart Disease (CHD) is published.
ImageCHD contains 110 3D Computed Tomography (CT) images covering most types of CHD, which is of decent size compared with existing medical imaging datasets. Classification of CHDs requires the identification of large structural changes without any local tissue changes, with limited data. It is an example of a larger class of problems that are quite difficult for current machine-learning based vision methods to solve.
Our dataset includes 110 CT images with labels. The label includes left ventricle (label: 1), right ventricle (label: 2), left atrium (label: 3), right atrium (label: 4), myocardium (label: 5), aorta (label: 6), and pulmonary artery (label: 7).
You notice other labels such 14 etc., you can just ignore them as they are labels corresponding to airways etc.
If you used our dataset, please consider to cite our paper in MICCAI 2020, Xiaowei Xu, Tianchen Wang, Haiyun Yuan, Qianjun Jia, Jianzheng Ceng, Yuhao Dong, Meiping Huang, and Jian Zhuang, Yiyu Shi, “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, 2020.
Update May 10th 2021: The diagnosis info of the dataset is updated (thanks to the help of Kadirbarut from Bilgiuzayi). Please check the xlsx file in the download dataset for more details.
HIGHLIGHT 20231101: We have deployed the dataset on Kaggle! https://www.kaggle.com/xiaoweixumedicalai/datasets
Please send emails to xiao.wei.xu@foxmail.com for any questions.
数据加载示例（图像类）
from PIL import Image
import glob, os
files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)
# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path) # 要求 子目录=类别
目录组织与标注格式以源站说明和下载后实际文件为准。
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当前公开数据无法满足您的算法精度？千方提供针对 艾森曼格综合征 的高质量、多模态真实临床数据定制解决方案。
