Objective: Automated segmentation tools are useful for calculating kidney volumes rapidly and accurately. Furthermore, these tools have the power to facilitate large-scale image-based artificial intelligence projects by generating input labels, such as for image registration algorithms. Prior automated segmentation models have largely ignored non-contrast computed tomography (CT) imaging. This work aims to implement and train a deep learning (DL) model to segment the kidneys and cystic renal lesions (CRLs) from non-contrast CT scans. Methods: Manual segmentation of the kidneys and CRLs was performed on 150 non-contrast abdominal CT scans. The data were divided into an 80/20 train/test split and a deep learning (DL) model was trained to segment the kidneys and CRLs. Various scoring metrics were used to assess model performance, including the Dice Similarity Coefficient (DSC), Jaccard Index (JI), and absolute and percent error kidney volume and lesion volume. Bland-Altman (B-A) analysis was performed to compare manual versus DL-based kidney volumes. Results: The DL model achieved a median kidney DSC of 0.934, median CRL DSC of 0.711, and total median study DSC of 0.823. Average volume errors were 0.9% for renal parenchyma, 37.0% for CRLs, and 2.2% overall. B-A analysis demonstrated that DL-based volumes tended to be greater than manual volumes, with a mean bias of +3.0 ml (+/- 2 SD of +/- 50.2 ml). Conclusion: A deep learning model trained to segment kidneys and cystic renal lesions on non-contrast CT examinations was able to provide highly accurate segmentations, with a median kidney Dice Similarity Coefficient of 0.934. Keywords: deep learning; kidney segmentation; artificial intelligence; convolutional neural networks.

翻译：目的：自动化分割工具对于快速准确地计算肾脏体积具有实用价值。此外，这些工具能够通过生成输入标签（如图像配准算法所需的标签）来推动大规模基于图像的人工智能项目。先前的自动化分割模型大多忽略了非增强计算机断层扫描（CT）成像。本研究旨在实现并训练一个深度学习（DL）模型，用于从非增强CT扫描中分割肾脏及肾囊性病变（CRLs）。方法：对150例非增强腹部CT扫描的肾脏及肾囊性病变进行手动分割。数据按80/20比例划分为训练集与测试集，并训练深度学习模型进行肾脏及肾囊性病变分割。采用多种评分指标评估模型性能，包括Dice相似系数（DSC）、Jaccard指数（JI）以及肾脏体积和病变体积的绝对误差与百分比误差。通过Bland-Altman（B-A）分析比较手动与深度学习计算的肾脏体积。结果：深度学习模型的中位肾脏DSC为0.934，中位肾囊性病变DSC为0.711，总体中位研究DSC为0.823。肾实质平均体积误差为0.9%，肾囊性病变为37.0%，总体为2.2%。B-A分析显示深度学习计算的体积倾向于大于手动体积，平均偏差为+3.0 ml（±2 SD：±50.2 ml）。结论：基于非增强CT检查训练的深度学习模型能够实现高度准确的肾脏及肾囊性病变分割，中位肾脏Dice相似系数达0.934。关键词：深度学习；肾脏分割；人工智能；卷积神经网络。