Grading and diagnosis of tumors in cancer patients have traditionally been done by examination of tissue specimens under a powerful microscope by expert pathologists. While this process continues to be widely applied in clinical settings, it is not scalable to translational and clinical research studies involving hundreds or thousands of tissue specimens. State-of-the-art digitizing microscopy instruments are capable of capturing high-resolution images of whole slide tissue specimens rapidly. Computer aided segmentation and classification has the potential to improve the tumor diagnosis and grading process as well as to enable quantitative studies of the mechanisms underlying disease onset and progression.
The objective of this challenge is to evaluate and compare classification algorithms and to encourage the biomedical imaging community to design and implement more accurate and efficient algorithms.
The challenge will evaluate the performance of automated classification algorithms when information from two types of imaging data – Radiology images and Pathology images – is used. Participants are asked to classify a cohort of lower grade glioma tumor cases into two sub-types: Oligodendroglioma and Astrocytoma.
The whole slide tissue images are stored in Aperio SVS format. There are open source tools and libraries that can read these images: OpenSlide (http://openslide.org) and Bio-Formats (https://www.openmicroscopy.org/site/support/bio-formats5.1/about/index.html).
Each participant will be required at the end of the test phase to submit a zip file (e.g., submission.zip) containing single tab-delimited text file (e.g., results.txt).
Each participant may submit up to 5 entries. The entry with the highest score will be used as the final score for said participant.
Each row of the text file corresponds to a case and stores (case ID, classification label) tuples. The classification label will be letter O for Oligodendroglioma and letter A for Astrocytoma. Case ID is the image filename prefix, e.g., case id for cbtc_test_16.svs is cbtc_test_16. The contents of an example result file are shown below:
cbtc_test_16 O cbtc_test_3 A cbtc_test_8 A … cbtc_test_19 OThe scoring for this sub-challenge will be computed as the number of correctly classified cases divided by the total number of cases. The datasets for this sub-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.
By participating in this challenge, each participant agrees to
1. Submit an extended abstract (up to 4-pages) before the end of the test phase describing their algorithm. Please upload the abstract report on dropbox folder
2. Present their algorithm and challenge results at the challenge meeting at MICCAI 2018, if the participant's entry scores in the top three among all the participants.
Start: June 29, 2018, 11:59 p.m.
Start: July 31, 2018, 11:59 p.m.
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