KiTS23
2023 Kidney and Kidney Tumor Segmentation Challenge
Contrast-enhanced preoperative abdominal CT of 599 patients treated for suspected kidney cancer at M Health Fairview (2010-2022), with kidney, tumor and cyst segmentations for 489 training cases and clinical metadata.
Overview
KiTS23 is the third Kidney Tumor Segmentation challenge from the University of Minnesota group, held at MICCAI 2023. It extends the earlier KiTS19 and KiTS21 collections with more patients and with scans in a second contrast phase, and is one of the largest openly available sets of annotated kidney tumor CT.
Composition
The challenge has 599 patients, each represented by the most recent contrast-enhanced CT taken before surgery. 489 cases with labels form the training set; the remaining 110 were held out as the test set. Every training case is labelled for kidney, tumor and cyst. A JSON file adds clinical data for the training cases, among them age at surgery, sex, BMI, comorbidities, surgical details, pathology (histologic subtype, ISUP grade, TNM stage) and survival. Most training cases had a malignant tumor; a minority were benign.
Acquisition
All patients underwent cryoablation, partial nephrectomy or radical nephrectomy for a suspected renal malignancy at an M Health Fairview medical center in Minnesota between 2010 and 2022. Unlike previous editions, which used only the late arterial (corticomedullary) phase, KiTS23 also includes nephrogenic-phase scans. Many scans were referred from outside hospitals, so scanners and protocols vary. Images are distributed as NIfTI through a Hugging Face repository and a download script.
Annotations
Trainees (residents, medical students and pre-medical undergraduates) delineated each region, guided by bounding boxes and with expert radiologists and urologic oncologists available for consultation. Unlike KiTS21, which had three independent delineations per region, KiTS23 has a single delineation. Evaluation uses three nested regions: kidney and masses, masses (tumor plus cyst) and tumor alone.
Known limitations
- All patients come from a single US health system.
- Single annotations mean no inter-rater variability estimate.
- Ages are recorded at nephrectomy; the oldest recorded age is 90 and a few training cases are children.
- Scanner and protocol details are not published per case.
- The CC BY-NC-SA license rules out commercial use without separate permission.
Cohort
Aggregate numbers from the sources below. Bars are relative to the 599 subjects.
Contrast / sequence
subjects, values can overlap
- Contrast CT 599 100%
Split
subjects
- Training 489 82%
- Test 110 18%
License and access
Our reading of the license, not legal advice. Before you use the data, read the original license and confirm that your use is allowed. We take no responsibility for how you use a dataset. Full disclaimer
Download without an account
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
Non-commercial use only, with credit. Anything you share that is built on the data must use the same license.
The kits23 GitHub repository code is MIT licensed. The README states that training a model solely to take part in the challenge is not considered commercial use, and that commercial users should contact Nicholas Heller.
What you can do
- No
- Conditional
- Yes
- Yes
What you can share
- Conditional
- Conditional
- Conditional
What you must do
- Yes
- Yes
- No
- No
- Manuscript review No
- Release code No
- Return results No
- Delete after use No
Limits
- No
- Location limits No
Citation
Heller N, Isensee F, Trofimova D, et al. The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT. arXiv:2307.01984 (2023). Cited as requested in the KiTS23 repository.
All numbers
Every number on this page, as stored in stats.csv, with its source.
| Measure | Breakdown | Value | Source |
|---|---|---|---|
| Subjects | total "599 cases", one preoperative scan per patient | 599 | kits23-page |
| Subjects | split=train | 489 | kits23-page |
| Subjects | split=test labels not released | 110 | kits23-page |
| Subjects | contrast=ct_contrast corticomedullary or nephrogenic phase | 599 | kits23-page |
| Subjects | split=train;sex=female one further training case is recorded as transgender (not listed) | 182 | kits23-clinical-json |
| Subjects | split=train;sex=male | 306 | kits23-clinical-json |
| Subjects | split=train;age=20-29 age at nephrectomy | 10 | kits23-clinical-json |
| Subjects | split=train;age=30-39 | 24 | kits23-clinical-json |
| Subjects | split=train;age=40-49 | 55 | kits23-clinical-json |
| Subjects | split=train;age=50-59 | 122 | kits23-clinical-json |
| Subjects | split=train;age=60-69 | 156 | kits23-clinical-json |
| Subjects | split=train;age=70-79 | 88 | kits23-clinical-json |
| Subjects | split=train;age=80-89 | 25 | kits23-clinical-json |
| Subjects | split=train;sex=female;age=30-39 | 11 | kits23-clinical-json |
| Subjects | split=train;sex=male;age=30-39 | 13 | kits23-clinical-json |
| Subjects | split=train;sex=female;age=40-49 | 17 | kits23-clinical-json |
| Subjects | split=train;sex=male;age=40-49 | 38 | kits23-clinical-json |
| Subjects | split=train;sex=female;age=50-59 | 42 | kits23-clinical-json |
| Subjects | split=train;sex=male;age=50-59 | 80 | kits23-clinical-json |
| Subjects | split=train;sex=female;age=60-69 | 65 | kits23-clinical-json |
| Subjects | split=train;sex=male;age=60-69 | 91 | kits23-clinical-json |
| Subjects | split=train;sex=female;age=70-79 | 30 | kits23-clinical-json |
| Subjects | split=train;sex=male;age=70-79 | 57 | kits23-clinical-json |
| Subjects | split=train;sex=male;age=80-89 | 16 | kits23-clinical-json |
| Subjects | split=train;condition=kidney_cancer malignant = true on pathology; 43 benign and 7 unknown | 439 | kits23-clinical-json |
Sources
The keys used in the table above.
- kits23-page KiTS23 challenge page website
- kits23-github neheller/kits23 GitHub repository README (license, dataset version) website
- kits23-clinical-json dataset/kits23.json in neheller/kits23 (CC BY-NC-SA 4.0), clinical data of the 489 training cases, counted per case computed