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先天性心脏病CT数据集
【数据集背景】
本数据集源站标题为《XiaoweiXu/ImageCHD-A-3D-Computed-Tomography-Image-Dataset-for-Classification-of-Congenital-Heart-Disease》。源站首次发布：2020-06-28。
【数据内容】
源站原始描述：# 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…
【适用方向】
影像类数据可用于医学图像分析模型（检测、分割、分类）的训练与评测，也可用于标注质量与成像参数相关性的研究。
【获取与许可】
源站页面：https://github.com/XiaoweiXu/ImageCHD-A-3D-Computed-Tomography-Image-Dataset-for-Classification-of-Congenital-Heart-Disease
【使用注意】
· 源站最后修改于 2023-11-02，引用时请注意版本时效。
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
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。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
⚡️ 需要数据支持或标注服务？立即联系我们获取专业方案。
