Automatic detection of kidney stones on plain CT images: a feasibility study with deep learning and thresholding methods
10.3760/cma.j.cn112149-20190630-00547
- VernacularTitle:用深度学习和阈值算法自动检出CT平扫图像中肾结石的可行性研究
- Author:
Yingpu CUI
1
;
Zhaonan SUN
;
Xiang LIU
;
Chao HAN
;
Xiaodong ZHANG
;
Xiaoying WANG
Author Information
1. 北京大学第一医院医学影像科 100034
- From:
Chinese Journal of Radiology
2020;54(9):869-873
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop and validate a cascaded deep learning algorithm for kidney stone detection on plain CT images.Methods:Plain CT images of the patients with kidney stones were retrospectively archived from January 2018 to July 2018 in Peking University First Hospital. The cases were divided into two datasets according to the date of the CT scanning: training dataset （ n=30) and held-out test dataset （ n=29). The development of the kidney stone detection method consisted of three steps. First, a U-Net model was trained on the training dataset for kidney segmentation, and the model′s performance was estimated with the dice coefficient. Second, the thresholding and region growing methods were used to detect the stones in the renal region predicted by the trained U-Net model. Third, the stones′ lengths （maximal, middle and minimal length) and CT attenuation were calculated and integrated into a structured report automatically. Using the radiologist′s labels and measurements (maximal, middle, minimal length and CT attenuation) as ground truth, the stone detection algorithm performance was evaluated with sensitivity, specificity and precision, and the stone measurement algorithm performance was evaluated with Bland-Altman plots. Results:The held-out test dataset consisted of 29 cases, containing 58 kidneys and 11 358 CT slices. The 38 kidneys containing 56 stones and 20 kidneys did not contain stones. The U-Net model showed good performance, with a mean dice coefficient of 0.96. And 10 945 of 11 358 CT slices had a dice coefficient no less than 0.90. The sensitivity, precision, and specificity of stone detection were 100% （38/38), 100% （38/38) and 100% （20/20) in the organ-level. The sensitivity and precision of stone detection were 100% （56/56) and 96.6% （56/58) in the lesion-level.Conclusion:A cascaded algorithm is constructed and can be used to detect kidney stones in plain CT images. The algorithm can improve efficiency with results automatically integrated into the structured report in clinical practice.
