Left Atrial Segmentation Challenge 2013: MRI training
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
This fileset is associated with the Left Atrial Segmentation Challenge 2013 (LASC'13). LASC'13 was part of the STACOM'13 workshop, held in conjunction with MICCAI'13. Seven international research groups, comprising 11 algorithms, participated in the challenge.
For a detailed report, please refer to:
Tobon-Gomez C, Geers AJ, Peters, J, Weese J, Pinto K, Karim R, Ammar M, Daoudi A, Margeta J, Sandoval Z, Stender B, Zheng Y, Zuluaga, MA, Betancur J, Ayache N, Chikh MA, Dillenseger J-L, Kelm BM, Mahmoudi S, Ourselin S, Schlaefer A, Schaeffter T, Razavi R, Rhode KS. Benchmark for Algorithms Segmenting the Left Atrium From 3D CT and MRI Datasets. IEEE Transactions on Medical Imaging, 34(7):1460–1473, 2015.
The challenge is also featured on http://www.cardiacatlas.org/challenges/left-atrium-segmentation-challenge/
The data and code of the challenge have been made publicly available to serve as a benchmark for left atrial segmentation algorithms. Code is hosted on https://github.com/catactg/lasc
Feel free to contact us with any questions.
This fileset consists of 10 MRI datasets for training segmentation algorithms. Included are the image and GT segmentation.
gt_binary.mhd + gt_binary.raw: Binary image representation of GT
image.mhd + image.raw: Image for training