KiTS19 Challenge Homepage
This site is the home to all information related to the 2019 Kidney Tumor Segmentation Challenge. For the most up-to-date information, please visit our announcements page.
There are more than 400,000 new cases of kidney cancer each year , and surgery is its most common treatment . Due to the wide variety in kidney and kidney tumor morphology, there is currently great interest in how tumor morphology relates to surgical outcomes, [3,4] as well as in developing advanced surgical planning techniques . Automatic semantic segmentation is a promising tool for these efforts, but morphological heterogeneity makes it a difficult problem.
The goal of this challenge is to accelerate the development of reliable kidney and kidney tumor semantic segmentation methodologies. We have produced ground truth semantic segmentations for arterial phase abdominal CT scans of 300 unique kidney cancer patients who underwent partial or radical nephrectomy at our institution. 210 of these have been released for model training and validation, and the remaining 90 will be held out for objective model evaluation (see the detailed data description).
A proposal was submitted and accepted to hold this challenge in conjunction with MICCAI 2019 in Shenzhen China. The top 5 scoring teams will be invited to give an oral presentation of their methods, and to coauthor a journal paper about the challenge.
|March 15, 2019||Release of Training Imaging and Labels|
|July 15, 2019
||Release of Testing Imaging, Submission Opens|
|July 29, 2019||Deadline for Submission of Test Predictions and Manuscript
|August 2, 2019||Results Announcement and Oral Invitations|
|October 13, 2019||Satellite Event at MICCAI 2019
University of Minnesota
Christopher Weight, MD, MS (Clinical Chair)
Nikolaos Papanikolopoulos, PhD (Computing Chair)
Arveen Kalapara, MBBS, DMedSci Candidate
Nicholas Heller, PhD Student
Niranjan Sathianathen, MBBS, DMedSci Candidate
||Intuitive Surgical has graciously sponsored a $5000 prize for the winning team.|
||This challenge was made possible by scholarships provided by Climb 4 Kidney Cancer (C4KC), an organization dedicated to advocacy for kidney cancer patients and the advancement of kidney cancer research.|
||This work was also supported by the National Cancer Institute of the National Institutes of Health under Award Number R01CA225435. The content is solely the responsibility of the organizers and does not necessarily represent the official views of the National Institutes of Health.|
1. “Kidney Cancer Statistics.” World Cancer Research Fund, 12 Sept. 2018, www.wcrf.org/dietandcancer/cancer-trends/kidney-cancer-statistics.
2. “Cancer Diagnosis and Treatment Statistics.” Stages | Mesothelioma | Cancer Research UK, 26 Oct. 2017, www.cancerresearchuk.org/health-professional/cancer-statistics/diagnosis-and-treatment.
3. Kutikov, Alexander, and Robert G. Uzzo. "The RENAL nephrometry score: a comprehensive standardized system for quantitating renal tumor size, location and depth." The Journal of urology 182.3 (2009): 844-853.
4. Ficarra, Vincenzo, et al. "Preoperative aspects and dimensions used for an anatomical (PADUA) classification of renal tumours in patients who are candidates for nephron-sparing surgery." European urology 56.5 (2009): 786-793.
5. Taha, Ahmed, et al. "Kid-Net: Convolution Networks for Kidney Vessels Segmentation from CT-Volumes." arXiv preprint arXiv:1806.06769 (2018).