Can I publish papers using the data or evaluation results?
Of course you can. Please cite the following papers when you use the data for publications:
 Xiahai Zhuang: Multivariate mixture model for myocardial segmentation combining multi-source images. IEEE Transactions on Pattern Analysis and Machine Intelligence (T PAMI), vol. 41, no. 12, 2933-2946, Dec 2019.
 Xiahai Zhuang: Multivariate mixture model for cardiac segmentation from multi-sequence MRI. International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.581-588, 2016.
Can I use semi-automatic segmentation algorithms in this challenge?
The challenge aims at both semi-automatic and fully-automatic segmentation methods. Two types of method will be ranked separately.
Why the three-sequence CMR images have the same resolution?
The data released here have been pre-processed using the MvMM method [1,2], to align the three-sequence CMR into a common space and to resample them into the same spatial resolution.
Why some cases have pretty poor image quality (lots of motion artifacts)?
All the data were collected based on in vivo clinical environment and the data were used in clinics. So the data had various image quality, some data were with relative poor quality. However, it is necessary to include these datasets to validate the robustness of the developed algorithms when it comes to real clinical usage.
Can I use our own training data?
We welcome all submissions which may use their own training data (either public or non-public) or focus on other issues of the cardiac image computing topic. However, we will rank separately for the teams using our provided training data and the teams using more than that.