The official repository of the 2023 Kidney Tumor Segmentation Challenge

Challenge Homepage

Current Dataset Version: 0.1.3

Most recent change (0.1.2 -> 0.1.3) was documentation only.

• April 14: Training dataset release
• July 14: Deadline for short paper
• July 21 - July 28: Prediction submissions accepted
• July 31: Results announced
• October 8 or 12: Satellite event at MICCAI 2023

Check here for the latest news about the KiTS23 dataset and starter code!

This repository is meant to serve two purposes:

1. To help you download the dataset
2. To allow you to benchmark your model using the official implementation of the metrics

We recommend starting by installing this kits23 package using pip. Once you've done this, you'll be able to use the command-line download entrypoint and call the metrics functions from your own codebase.

This should be as simple as cloning and installing with pip.

git clone https://github.com/neheller/kits23
cd kits23
pip3 install -e .

We're running Python 3.10.6 on Ubuntu and we suggest that you do too. If you're running a different version of Python 3 and you discover a problem, please submit an issue and we'll try to help. Python 2 or earlier is not supported. If you're running Windows or MacOS, we will do our best to help but we have limited ability to support these environments.

Once the kits23 package is installed, you should be able to run the following command from the terminal.

kits23_download_data

This will place the data in the dataset/ folder.

We provide a reference implementation for the metrics and the ranking scheme so that participants know exactly how their algorithm is going to be validated. We strongly encourage using these implementations for model selection during development. In order to do this, we recommend training all your models as cross-validations, so that you have predictions for the entire training set with which you can meaningfully evaluate your approaches.

After installing the KiTS23 repository, the following console command is available to you: kits23_compute_metrics. You can use it to evaluate predictions against the training ground truth. It's as simple as

kits23_compute_metrics FOLDER_WITH_PREDICTIONS -num_processes XX

This will produce a evaluation.csv in FOLDER_WITH_PREDICTIONS with the computed Dice and surface Dice scores.

Ranking is based on first averaging your dice and surface dice scores across all cases and HECs, resulting in two values: your average Dice and average Surface Dice. We then use 'rank-then-aggregate' to merge these metrics into a final ranking.

Here are the steps you need to do to run the ranking locally (for example, to find the best configuration for your submission):

1. Execute generate_summary_csv, located in ranking.py. The documentation will tell you how
2. Then use rank_participants from the same file with the summary.csv generated by generate_summary_csv to generate the final ranking.

The code in this repository is under an MIT License. The data that this code downloads is under a CC BY-NC-SA (Attribution-NonCommercial-ShareAlike) license. If you would like to use this data for commercial purposes, please contact Nicholas Heller at [email protected]. Please note, we do not consider training a model for the sole purpose of participation in this competition to be commercial use. Therefore, industrial teams are strongly encouraged to participate. If you are an academic researcher interested in using this dataset in your work, you needn't ask for permission.

If this project is useful to your research, please cite our most recent KiTS challenge paper in Medical Image Analysis [html] [pdf].

@misc{heller2023kits21,
      title={The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT}, 
      author={Nicholas Heller and Fabian Isensee and Dasha Trofimova and Resha Tejpaul and Zhongchen Zhao and Huai Chen and Lisheng Wang and Alex Golts and Daniel Khapun and Daniel Shats and Yoel Shoshan and Flora Gilboa-Solomon and Yasmeen George and Xi Yang and Jianpeng Zhang and Jing Zhang and Yong Xia and Mengran Wu and Zhiyang Liu and Ed Walczak and Sean McSweeney and Ranveer Vasdev and Chris Hornung and Rafat Solaiman and Jamee Schoephoerster and Bailey Abernathy and David Wu and Safa Abdulkadir and Ben Byun and Justice Spriggs and Griffin Struyk and Alexandra Austin and Ben Simpson and Michael Hagstrom and Sierra Virnig and John French and Nitin Venkatesh and Sarah Chan and Keenan Moore and Anna Jacobsen and Susan Austin and Mark Austin and Subodh Regmi and Nikolaos Papanikolopoulos and Christopher Weight},
      year={2023},
      eprint={2307.01984},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}