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We had a total set of 150 MRI volumes available for this challenge. All datasets were completely anonymized and originated from the surgical planning program of Biomet, Inc.
All data is stored in Meta format containing an ASCII readable header and a separate raw image data file. This format is ITK compatible. Full documentation is available here. An application that can read the data is MITK-3M3. If you want to write your own code to read the data, note that in the header file you can find the dimensions of each file. In the raw file the values for each voxel are stored consecutively with index running first over x, then y, then z. The pixel type is short for the image data and unsigned char for the segmentations of the training data. Segmentations are multi-label images with the following codes: 0=background, 1=femur bone, 2=femur cartilage, 3=tibia bone, 4=tibia cartilage.
The last training data set (images 61-100) includes corresponding ROI images; these specify regions of interest where cartilage segmentations will be evaluated. Segmentations of the femoral cartilage will be evaluated in regions where bit 1 is set (i.e. values 1 and 3). Segmentations of the tibia cartilage will be evaluated in regions where bit 2 is set (i.e. values 2 and 3). ROI images are required by the provided evaluation source code, i.e. you can use the last training data set to estimate the performance of your algorithm before submission.