C4KC KiTS Challenge Kidney Tumor Segmentation Dataset

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 association between kidney tumor morphometry and clinical outcomes is difficult due to data scarcity and the laborious nature of manually quantifying imaging predictors. Reliable semantic segmentation of kidneys and kidney tumors is a powerful 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. With the presence of comorbidities and clinical 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 in conjunction with semantic segmentation masks.

增强计算机断层扫描（Computed Tomography，简称CT）所显示的肾脏肿瘤形态计量学特征，是围绕该病灶诊断与治疗的临床决策中的关键影响因素。由于数据稀缺且手动量化影像预测特征的工作繁重，针对肾脏肿瘤形态计量学特征与临床结局之间关联的定量研究开展困难。对肾脏及肾脏肿瘤进行可靠的语义分割，是自动量化多种形态计量学特征的有力手段，但目前尚无大规模标注数据集可供训练此类任务的模型。本文提出KiTS19挑战赛数据集：该数据集包含2010年至2018年间在本中心因肾脏肿瘤接受肾切除术的300例患者的多期CT影像、分割掩码以及完整的临床结局数据。其中210例（占比70%）患者被随机选取，作为2019年MICCAI KiTS肾脏肿瘤分割挑战赛的训练集。结合合并症与临床结局数据，该数据集不仅可用于语义分割模型的性能基准测试，还可用于开发和研究结合影像与语义分割掩码的生物标志物。

搜集汇总

背景与挑战

背景概述

C4KC KiTS挑战肾脏肿瘤分割数据集是一个包含300名肾肿瘤患者的多期CT影像、分割掩码和临床结果的集合，旨在训练和评估语义分割模型以自动量化肿瘤形态特征。该数据集特别为2019年MICCAI KiTS挑战提供210例训练数据，不仅支持分割模型基准测试，还可用于结合影像和分割掩码开发生物标志物研究。