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Abstract: SA-PO005

Fully Automated Segmentation of Kidneys and Exophytic Cysts Using Deep Convolutional Neural Networks in Subjects With Autosomal Dominant Polycystic Kidney Disease

Session Information

  • Bioengineering
    November 05, 2022 | Location: Exhibit Hall, Orange County Convention Center‚ West Building
    Abstract Time: 10:00 AM - 12:00 PM

Category: Bioengineering

  • 300 Bioengineering


  • Kim, Youngwoo, Korea Institute of Industrial Technology, Cheonan, Korea (the Republic of)
  • Bu, Seon-ah, Korea Institute of Industrial Technology, Cheonan, Korea (the Republic of)
  • Bae, Kyongtae Ty, The University of Hong Kong Li Ka Shing Faculty of Medicine, Hong Kong, Hong Kong

Group or Team Name


In diagnosing autosomal dominant polycystic kidney disease (ADPKD), total kidney volume (TKV) is a critical imaging biomarker. Nevertheless, manual computation of TKV, particularly with the exclusion of exophytic cysts, is laborious and time consuming. We developed a fully automated segmentation method for TKV using a deep learning network to selectively segment kidney regions while excluding exophytic cysts.


We used abdominal T2-weighted magnetic resonance images from 210 ADPKD subjects who were divided into two groups: 157 to train the network and 53 to test. With a 3D U-Net architecture using dataset fingerprints, the network was trained by K-fold cross-validation in that 80% out of 157 cases were for training and the remaining 20% cases for validation. To assess the performance of the automated segmentation method in reference to the manual method, three metrics were analyzed: Dice similarity coefficient (DSC), intra-class correlation coefficient (ICC), and Bland-Altman analysis.


Excellent geometric concordance was achieved between the automated and manual reference methods (DSC: 0.962±0.018, on average) on the test datasets with widely varying volumes of kidneys (1058.5±706.8 ml, range: 178.9–2776.0 ml) and exophytic cysts (549.0±559.1 ml, range: 113.4–2497.6 ml). The ICC was 0.9994 (P < 0.001; CI: 0.9991–0.9996) with a minimum bias of -2.424 ml (95% limits of agreement: -49.80, 44.95).


A fully automated segmentation method that we developed measures TKV with exclusion of exophytic cysts as accurately as the level of a human expert. This technique will be useful in clinical studies that require automated computation of TKV to evaluate progression and treatment response of ADPKD.


  • Government Support – Non-U.S.