Automatic Segmentation of Kidneys and Kidney Tumors: The KiTS19 International Challenge

Niranjan J Sathianathen, Nick Heller, Resha D Tejpaul, Bethany D Stai, Arveen Kalapara, Jack Rickman, Joshua Dean, Makinna Oestreich, Paul J Blake, Heather Kaluzniak, Shaneabbas Raza, Joel Rosenberg, Keenan Moore, Edward G Walczak, Zachary Rengel, Zach Edgerton, Ranveer Vasdev, Matthew Peterson, Sean McSweeney, Sarah PetersonNikolaos Papanikolopoulos, Christopher J Weight

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Purpose: Clinicians rely on imaging features to calculate complexity of renal masses based on validated scoring systems. These scoring methods are labor-intensive and are subjected to interobserver variability. Artificial intelligence has been increasingly utilized by the medical community to solve such issues. However, developing reliable algorithms is usually time-consuming and costly. We created an international community-driven competition (KiTS19) to develop and identify the best system for automatic segmentation of kidneys and kidney tumors in contrast CT and report the results. Methods: A training and test set of CT scans that was manually annotated by trained individuals were generated from consecutive patients undergoing renal surgery for whom demographic, clinical and outcome data were available. The KiTS19 Challenge was a machine learning competition hosted on in conjunction with an international conference. Teams were given 3 months to develop their algorithm using a full-annotated training set of images and an unannotated test set was released for 2 weeks from which average Sørensen-Dice coefficient between kidney and tumor regions were calculated across all 90 test cases. Results: There were 100 valid submissions that were based on deep neural networks but there were differences in pre-processing strategies, architectural details, and training procedures. The winning team scored a 0.974 kidney Dice and a 0.851 tumor Dice resulting in 0.912 composite score. Automatic segmentation of the kidney by the participating teams performed comparably to expert manual segmentation but was less reliable when segmenting the tumor. Conclusion: Rapid advancement in automated semantic segmentation of kidney lesions is possible with relatively high accuracy when the data is released publicly, and participation is incentivized. We hope that our findings will encourage further research that would enable the potential of adopting AI into the medical field.

Original languageEnglish (US)
Article number797607
JournalFrontiers in Digital Health
StatePublished - Jan 4 2022

Bibliographical note

Publisher Copyright:
Copyright © 2022 Sathianathen, Heller, Tejpaul, Stai, Kalapara, Rickman, Dean, Oestreich, Blake, Kaluzniak, Raza, Rosenberg, Moore, Walczak, Rengel, Edgerton, Vasdev, Peterson, McSweeney, Peterson, Papanikolopoulos and Weight.


  • ct scans
  • kidney tumors
  • medical images
  • renal mass
  • semantic segmentation

PubMed: MeSH publication types

  • Journal Article


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