Expert-level segmentation using deep learning for volumetry of polycystic kidney and liver

Tae Young Shin, Hyunsuk Kim, Joong Hyup Lee, Jong Suk Choi, Hyun Seok Min, Hyungjoo Cho, Kyungwook Kim, Geon Kang, Jungkyu Kim, Sieun Yoon, Hyungyu Park, Yeong Uk Hwang, Hyo Jin Kim, Miyeun Han, Eunjin Bae, Jong Woo Yoon, Koon Ho Rha, Yong Seong Lee

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Purpose: Volumetry is used in polycystic kidney and liver diseases (PKLDs), including autosomal dominant polycystic kidney disease (ADPKD), to assess disease progression and drug efficiency. However, since no rapid and accurate method for volumetry has been developed, volumetry has not yet been established in clinical practice, hindering the development of therapies for PKLD. This study presents an artificial intelligence (AI)-based volumetry method for PKLD. Materials and Methods: The performance of AI was first evaluated in comparison with ground-truth (GT). We trained a V-net-based convolutional neural network on 175 ADPKD computed tomography (CT) segmentations, which served as the GT and were agreed upon by 3 experts using images from 214 patients analyzed with volumetry. The dice similarity coefficient (DSC), interob-server correlation coefficient (ICC), and Bland–Altman plots of 39 GT and AI segmentations in the validation set were compared. Next, the performance of AI on the segmentation of 50 random CT images was compared with that of 11 PKLD specialists based on the resulting DSC and ICC. Results: The DSC and ICC of the AI were 0.961 and 0.999729, respectively. The error rate was within 3% for approximately 95% of the CT scans (error<1%, 46.2%; 1%≤error<3%, 48.7%). Compared with the specialists, AI showed moderate performance. Further-more, an outlier in our results confirmed that even PKLD specialists can make mistakes in volumetry. Conclusions: PKLD volumetry using AI was fast and accurate. AI performed comparably to human specialists, suggesting its use may be practical in clinical settings.

Original languageEnglish
Pages (from-to)555-564
Number of pages10
JournalInvestigative and clinical urology
Volume61
Issue number6
DOIs
Publication statusPublished - 2020

Bibliographical note

Funding Information:
This research was supported by a grant of the National Research Foundation of Korea (NRF-2017R1C1B1008260, NRF-2016R1D1A1B03934173) and the Hallym University Research Fund 2017 (HURF-2017-36), and the Young Investigator Research Grant from the Korean Society of Nephrology (2019).

Publisher Copyright:
© The Korean Urological Association.

All Science Journal Classification (ASJC) codes

  • Urology

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