Purpose

To develop and validate a deep learning and thresholding-based model for automatic kidney stone detection and scoring according to S.T.O.N.E. nephrolithometry.

Procedures

Abdominal noncontrast computed tomography (NCCT) images were retrospectively archived from February 2018 to April 2019 for three parts: a segmentation dataset (n = 167), a hydronephrosis classification dataset (n = 282), and test dataset (n = 117). The model consisted of four steps. First, the 3D U-Nets for kidney and renal sinus segmentation were developed. Second, the deep 3D dual-path networks for hydronephrosis grading were developed. Third, the thresholding methods were used to detect and segment stones in the renal sinus region. The stone size, CT attenuation, and tract length were calculated from the segmented stone region. Fourth, the stone’s location was determined. The stone detection performance was estimated with sensitivity and positive predictive value (PPV). The hydronephrosis grading and stone size, tract length, number of involved calyces, and essence grading were estimated with the area under the curve (AUC) method and linear-weighted κ statistics, respectively.

Results

The stone detection algorithm reached a sensitivity of 95.9 % (236/246) and a PPV of 98.7 % (236/239). The hydronephrosis classification algorithm achieved an AUC of 0.97. The scoring model results showed good agreement with radiologist results for the stone size, tract length, number of involved calyces, and essence grading (κ = 0.95, 95 % confidence interval [CI]: 0.92, 0.98; κ = 0.97, 95 % CI: 0.95, 1.00; κ = 0.95, 95 % CI: 0.92, 0.98; and κ = 0.97, 95 % CI: 0.94, 1.00), respectively.

Conclusions

The scoring model was constructed that can automatically detect and score stones in NCCT images.

中文翻译：

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使用 STONE 肾光度计在非对比 CT 图像上自动检测和评分肾结石：结合深度学习和阈值方法

目的

开发和验证基于深度学习和阈值的模型，用于根据 STONE 肾光度计进行自动肾结石检测和评分。

程序

2018 年 2 月至 2019 年 4 月的腹部非对比计算机断层扫描 (NCCT) 图像回顾性存档，分为三个部分：分割数据集 ( n  = 167)、肾积水分类数据集 ( n  = 282) 和测试数据集 ( n = 117)。该模型包括四个步骤。首先，开发了用于肾脏和肾窦分割的 3D U-Nets。其次，开发了用于肾积水分级的深度 3D 双路径网络。第三，采用阈值法对肾窦区结石进行检测和分割。从分割的结石区域计算结石大小、CT 衰减和管道长度。第四，确定石头的位置。用敏感性和阳性预测值 (PPV) 估计结石检测性能。分别用曲线下面积（AUC）法和线性加权κ统计估计肾积水分级和结石大小、管长、受累肾盏数和实质分级。

结果

结石检测算法达到 95.9 % (236/246) 的灵敏度和 98.7 % (236/239) 的 PPV。肾积水分类算法的 AUC 为 0.97。评分模型结果与放射科医师在结石大小、管道长度、受累肾盏数量和本质分级方面的结果具有良好的一致性（κ  = 0.95, 95 % 置信区间 [CI]: 0.92, 0.98; κ  = 0.97, 95 % CI : 0.95, 1.00; κ  = 0.95, 95 % CI: 0.92, 0.98; 和κ  = 0.97, 95 % CI: 0.94, 1.00)。

结论

构建了能够自动检测和评分NCCT图像中的结石的评分模型。