The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

TL;DR

The KiTS21 challenge advanced kidney and tumor segmentation in CT scans by introducing a larger, more diverse dataset, novel annotation methods, and emphasizing generalization to new populations, leading to performance nearing human levels.

Contribution

This paper introduces the KiTS21 challenge with innovative annotation techniques, a larger dataset, and a focus on generalization, significantly improving kidney tumor segmentation performance.

Findings

Top methods improved over 2019 state of the art

Performance neared human-level accuracy

Generalization to external data was successfully demonstrated

Abstract

This paper presents the challenge report for the 2021 Kidney and Kidney Tumor Segmentation Challenge (KiTS21) held in conjunction with the 2021 international conference on Medical Image Computing and Computer Assisted Interventions (MICCAI). KiTS21 is a sequel to its first edition in 2019, and it features a variety of innovations in how the challenge was designed, in addition to a larger dataset. A novel annotation method was used to collect three separate annotations for each region of interest, and these annotations were performed in a fully transparent setting using a web-based annotation tool. Further, the KiTS21 test set was collected from an outside institution, challenging participants to develop methods that generalize well to new populations. Nonetheless, the top-performing teams achieved a significant improvement over the state of the art set in 2019, and this performance is shown to inch ever closer to human-level performance. An in-depth meta-analysis is presented describing which methods were used and how they faired on the leaderboard, as well as the characteristics of which cases generally saw good performance, and which did not. Overall KiTS21 facilitated a significant advancement in the state of the art in kidney tumor segmentation, and provides useful insights that are applicable to the field of semantic segmentation as a whole.