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Automatic Urinary Stone Detection System for Abdominal Non-Enhanced CT Images Reduces the Burden on Radiologists

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Abstract

To develop a fully automatic urinary stone detection system (kidney, ureter, and bladder) and to test it in a real clinical environment. The local institutional review board approved this retrospective single-center study that used non-enhanced abdominopelvic CT scans from patients admitted urology (uPatients) and emergency (ePatients). The uPatients were randomly divided into training and validation sets in a ratio of 3:1. We designed a cascade urinary stone map location-feature pyramid networks (USm-FPNs) and innovatively proposed a ureter distance heatmap method to estimate the ureter position on non-enhanced CT to further reduce the false positives. The performances of the system were compared using the free-response receiver operating characteristic curve and the precision-recall curve. This study included 811 uPatients and 356 ePatients. At stone level, the cascade detector USm-FPNs has the mean of false positives per scan (mFP) 1.88 with the sensitivity 0.977 in validation set, and mFP was further reduced to 1.18 with the sensitivity 0.977 after combining the ureter distance heatmap. At patient level, the sensitivity and precision were as high as 0.995 and 0.990 in validation set, respectively. In a real clinical set of ePatients (27.5% of patients contain stones), the mFP was 1.31 with as high as sensitivity 0.977, and the diagnostic time reduced by > 20% with the system help. A fully automatic detection system for entire urinary stones on non-enhanced CT scans was proposed and reduces obviously the burden on junior radiologists without compromising sensitivity in real emergency data.

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Data Availability

A data supporting this research is available from the corresponding author on reasonable request.

Abbreviations

CNNs:

Convolutional neural networks

ePatients:

Emergency patients

FPN:

Feature pyramid network

FROC:

Free-response receiver operating characteristic curve

mFP:

Mean of false positives per scan

PRC:

Precision-recall curve

TP:

True positive

USm-FPNs:

Cascade urinary stone map location-feature pyramid networks

uPatients:

Urology patients

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Author notes

  1. Zhaoyu Xing and Zuhui Zhu contributed equally to this work.

Authors and Affiliations

  1. Department of Urology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China

    Zhaoyu Xing

  2. Department of Radiology, Nantong Hospital of Traditional Chinese Medicine, Nantong, Jiangsu, China

    Zuhui Zhu

  3. Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China

    Zhenxing Jiang, Wei Xing, Liang Pan & Jiule Ding

  4. Department of Radiology, Fourth Affiliated Hospital of Harbin Medical University, Harbin, China

    Jingshi Zhao

  5. Department of Radiology, People’s Hospital of Pengzhou, Chengdu, Sichuan, China

    Qin Chen

  6. Department of Research Center, Shanghai United Imaging Intelligence Co. Ltd, Shanghai, China

    Yan Zeng & Aie Liu

Authors
  1. Zhaoyu Xing
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  2. Zuhui Zhu
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  8. Yan Zeng
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  9. Aie Liu
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  10. Jiule Ding
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Corresponding authors

Correspondence to Yan Zeng, Aie Liu or Jiule Ding.

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This retrospective cross-section study was approved by the local institutional review board. Informed consent was waived for the retrospective study.

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Xing, Z., Zhu, Z., Jiang, Z. et al. Automatic Urinary Stone Detection System for Abdominal Non-Enhanced CT Images Reduces the Burden on Radiologists. J Digit Imaging. Inform. med. 37, 444–454 (2024). https://doi.org/10.1007/s10278-023-00946-2

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  • DOI: https://doi.org/10.1007/s10278-023-00946-2

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