The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

TL;DR

The KiTS19 dataset provides a large, annotated collection of kidney tumor CT images, segmentation masks, and clinical outcomes to advance automated segmentation and morphometric analysis for kidney cancer diagnosis and treatment.

Contribution

This paper introduces the KiTS19 dataset, a large publicly available collection of multi-phase CT scans, segmentation masks, and clinical data for kidney tumors, enabling improved model training and biomarker research.

Findings

Dataset includes 300 cases with detailed annotations and clinical outcomes.

70% of data (210 cases) released publicly for benchmarking.

Facilitates development of automated segmentation and morphometric analysis.

Abstract

The morphometry of a kidney tumor revealed by contrast-enhanced Computed Tomography (CT) imaging is an important factor in clinical decision making surrounding the lesion's diagnosis and treatment. Quantitative study of the relationship between kidney tumor morphology and clinical outcomes is difficult due to data scarcity and the laborious nature of manually quantifying imaging predictors. Automatic semantic segmentation of kidneys and kidney tumors is a promising tool towards automatically quantifying a wide array of morphometric features, but no sizeable annotated dataset is currently available to train models for this task. We present the KiTS19 challenge dataset: A collection of multi-phase CT imaging, segmentation masks, and comprehensive clinical outcomes for 300 patients who underwent nephrectomy for kidney tumors at our center between 2010 and 2018. 210 (70%) of these patients were selected at random as the training set for the 2019 MICCAI KiTS Kidney Tumor Segmentation Challenge and have been released publicly. With the presence of clinical context and surgical outcomes, this data can serve not only for benchmarking semantic segmentation models, but also for developing and studying biomarkers which make use of the imaging and semantic segmentation masks.