- A blog post by Chuck-Hou Yee, an Insight AI Fellow about deep learning applied to the RVSC dataset, sept 2017
- Phi Vu Tran A Fully Convolutional Neural Network for Cardiac Segmentation in Short-Axis MRI, arXiv:1604.00494, April 2016 [cs.CV]
- MR Avendi, Arash Kheradvar, Hamid Jafarkhani, Fully automatic segmentation of heart chambers in cardiac MRI using deep learning, Journal of Cardiovascular Magnetic Resonance, December 2016, 18:P351
- Punithakumar, K., Noga, M., Ben Ayed, I., Boulanger, P. Right ventricular segmentation in cardiac MRI with moving mesh correspondences, Computerized Medical Imaging and Graphics, Volume 43, July 2015, Pages 15–25.
- O. Moolan-Feroze, M. Mirmehdi, M. Hamilton, and C. Bucciarelli-Ducci, “Segmentation of the right ventricle using diffusion maps and markov random fields,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014. Springer, 2014, pp. 682–689.
- Ringenberg, J., Deo, M., Devabhaktuni, V., Berenfeld, O., Boyers, P., Gold, J. Fast, accurate, and fully automatic segmentation of the right ventricle in short-axis cardiac MRI. Computerized Medical Imaging and Graphics volume 38, issue 3, year 2014, pp. 190 – 201
- Labrador, A. A., Martnez, F., Castro, E. A novel right ventricle segmentation approach from local spatio-temporal MRI information. In: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications (CIARP), La Havana, Cuba. Vol. 8259 of LNCS. pp. 206-213, 2013
- Mahapatra, D., Buhmann, J. M. Automatic cardiac RV segmentation using semantic information with graph cuts. In: IEEE International Symposium Biomedical Imaging (ISBI’13). pp. 1094-1097, 2013
[Sept 3rd, 2014] Since the MICCAI’12 workshop, RVSC data have been requested 33 times by researchers from around the world. To reviewers of papers which contain experiments on RVSC data: results should be given on Test1 and Test2 sets (32 patients) only, and it should be explicit that evaluation was performed by the organizers, and the results were given to the authors.