Welcome to the Gleason 2019 Challenge¶
This challenge is part of the MICCAI 2019 Conference to be held from October 13 to 17 in Shenzhen, China.
This challenge will be one of the three challenges under the MICCAI 2019 Grand Challenge for Pathology.
Important Dates¶
May 15, 2019 --- The first training dataset will be released.
June 01, 2019 --- The second training dataset will be released.
June 15, 2019 --- The test dataset will be released.
September 15, 2019 --- Submission deadline
**September 27, 2019 --- DEADLINE EXTENDED **
September 30, 2019 --- Results will be announced.
Background¶
Prostate Cancer (PCa) is the sixth most common and second deadliest cancer among men worldwide. There exist various techniques for PCa detection and staging. However, microscopic inspection of stained biopsy tissue by expert pathologists is the most accurate method. Based on the observable histological patterns, each region of the tissue is assigned a Gleason grade of 1 to 5. The final Gleason score is reported as the sum of the most prominent and second most prominent patterns; e.g., a tissue with the most prominent pattern of Gleason grade of 4 and the second most prominent pattern of Gleason grade of 3 will have a Gleason score of 4+3.
This challenge aims at the automatic Gleason grading of prostate cancer from H&E-stained histopathology images. This task is of critical importance because Gleason score is a strong prognostic predictor. On the other hand, it is very challenging because of the large degree of heterogeneity in the cellular and glandular patterns associated with each Gleason grade, leading to significant inter-observer variability, even among expert pathologists.
Gleason grading of prostate cancer is usually performed via visual inspection (with a microscope) of the prostate tissue by expert pathologists. However, this is a time-consuming task and suffers from very high inter-observer variability. Automatic computer-aided methods have the potential for improving the speed, accuracy, and reproducibility of the results.
Data¶
Data used in this challenge consists of a set of tissue micro-array (TMA) images. Each TMA image is annotated in detail by several expert pathologists.
Objectives¶
This challenge will provide a unique dataset and evaluation framework for the important and challenging task of prostate cancer Gleason grading. It will help establish a benchmark for assessing and comparing the state of the art image analysis and machine learning-based algorithms for this challenging task. It will also help evaluate the accuracy and robustness of these computerized methods against the opinion of multiple human experts. Given the critical importance of prostate cancer and extreme utility of Gleason grade system for detection and diagnosis of prostate cancer, the results of this challenge can be of utmost utility for medical community.¶
Tasks
The challenge involves two separate tasks:
Task 1: Pixel-level Gleason grade prediction
Task 2: Core-level Gleason score prediction
References
If you use this dataset, please cite the following:
Nir G, Hor S, Karimi D, Fazli L, Skinnider BF, Tavassoli P, Turbin D, Villamil CF, Wang G, Wilson RS, Iczkowski KA. Automatic grading of prostate cancer in digitized histopathology images: Learning from multiple experts. Medical image analysis. 2018 Dec 1;50:167-80.
Karimi D, Nir G, Fazli L, Black PC, Goldenberg L, Salcudean SE. Deep Learning-Based Gleason Grading of Prostate Cancer From Histopathology Images—Role of Multiscale Decision Aggregation and Data Augmentation. IEEE journal of biomedical and health informatics. 2019 Sep 30;24(5):1413-26.