A rapid automated segmentation method for total kidney volume measurement on unenhanced computed tomography in autosomal dominant polycystic kidney disease.

Yingying Hu¹, Tianpei Zhao¹, Yunpeng Gao¹, Peiyao Zhang¹, Tongxi Liu¹, Yue Lin¹, Shuya Gao¹, Tongyin Zhang¹, Yinghao Xu², Bing Li², Yu Zhang², Sheng Xie¹, Li Zhou³, Hongliang Sun⁴

¹Radiology, China-Japan Friendship Hospital, 100029, No. 2, Yinghua East Street, Chaoyang District, Beijing, China.

²CT Clinical Research Department, CT Business Unit, Canon Medical Systems (China) CO., LTD., 100015, Beijing, China.

³Nephrology, China-Japan Friendship Hospital, 100029, No. 2, Yinghua East Street, Chaoyang District, Beijing, China. zhouli_zryy@163.com.

⁴Radiology, China-Japan Friendship Hospital, 100029, No. 2, Yinghua East Street, Chaoyang District, Beijing, China. stentorsun@gmail.com.

PubMed DOI

Abstract

PURPOSE: To develop and validate a fully automated deep learning-based method for total kidney volume (TKV) measurement from unenhanced computed tomography (CT) images in patients with autosomal dominant polycystic kidney disease (ADPKD). METHODS: A fully automated kidney segmentation model based on the nnU-Net framework was developed using unenhanced CT images from 236 patients, including 111 pa…

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