Extended Wingspan Cardiothoracic Ratio (CTR) Estimation Dataset used in the paper Unsupervised Domain Adaptation for Automatic Estimation of Cardiothoracic Ratio and Neural Architecture Search for Adversarial Medical Image Segmentation. The extension includes additional 38 patients and chest organ segmenation masks for all patients.
The dataset contains 259 patients' chest X-ray images, chest organ segmentation masks, and key points for CTR estimation.
The full dataset can be downloaded vis this link.
Each patient has an independent folder with the folder name as his ID. In each folder, there are original.dcm, left_lung.png, right_lung.png, heart.png, and one key_points.txt.
In key_points.txt, you will see name,x,y. name is the organ name, and x and y are the normalized pixel coordinates for the width and height. (x=0, y=0) stands for the upper left corner and (x=1, y=1) stands for the lower right corner.
The downsampled dataset with fixed resolution 512 x 512 is provided within the repository. In addition, another popular chest organ segmentation dataset Japanese Society of Radiological Technology(JSRT) is provided here as a comparison.
Two datasets wingspan and jsrt have the same structure. Each dataset has two folders png and mask, where mask has three sub-folders left_lung, right_lung and heart.
Python functions for CTR estimation is provided in utils.py.
If you find the work and dataset are useful in your research, please cite:
@inproceedings{dong2018unsupervised, title={Unsupervised domain adaptation for automatic estimation of cardiothoracic ratio}, author={Dong, Nanqing and Kampffmeyer, Michael and Liang, Xiaodan and Wang, Zeya and Dai, Wei and Xing, Eric}, booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention}, pages={544--552}, year={2018}, organization={Springer} }
@inproceedings{dong2019neural, title={Neural architecture search for adversarial medical image segmentation}, author={Dong, Nanqing and Xu, Min and Liang, Xiaodan and Jiang, Yiliang and Dai, Wei and Xing, Eric}, booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention}, pages={828--836}, year={2019}, organization={Springer} }