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The datasets for this challenge are Radiology and Pathology images obtained from the same patients. Each case corresponds to a single patient. There is one Radiology image and one whole slide tissue image for each case. The training set contains a total of 32 cases: 16 cases that are classified by pathologists as Oligodendroglioma and 16 cases classified as Astrocytoma. The test set will have 20 cases. Please note that the number of cases in the test set may not be equally partitioned between the two sub-types.
The whole slide tissue images are stored in Aperio SVS format. There are open source tools and libraries that can read these images: OpenSlide and Bio-Formats.

The characteristics of cancer nuclei are central components in many aspects of pathology classification and nuclear features, combined with “omics” have been shown by many research groups to be linked to patient outcome. Although there are many important and predictive features, characteristics of cancer nuclei are overall the most important. Virtually without exception, all commercial systems and academic groups in the digital Pathology area make use of nuclear segmentation algorithms. Given this, ability to segment and then classify nuclei is a key task.

The goal of this challenge is to evaluate the performance of algorithms for detection and segmentation of nuclear material in a tissue image. Participants are asked to detect and segment all the nuclei in a given set of image tiles extracted from whole slide tissue images. The algorithm results will be compared with consensus pathologist segmented sub regions. Winners will be ranked based on their nuclei segmentation best matching the reference standards. The reference standard will be pathologist generated nuclear segmentation on select regions of TCGA Glioma whole slide images for the challenge.