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ORIGINAL RESEARCH article

Front. Physiol.
Sec. Computational Physiology and Medicine
Volume 15 - 2024 | doi: 10.3389/fphys.2024.1432121

A method for detecting ureteral stent encrustations in medical CT images based on Mask-RCNN and 3D morphological analysis

Provisionally accepted
Hongji Hu Hongji Hu *Minbo Yan Minbo Yan Zicheng Liu Zicheng Liu Junliang Qiu Junliang Qiu Yingbo Dai Yingbo Dai Yuxin Tang Yuxin Tang
  • The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China

The final, formatted version of the article will be published soon.

    Objective: To develop and validate a method for detecting ureteral stent encrustations in medical CT images based on Mask-RCNN and 3D morphological analysis.All 222 cases of ureteral stent data were obtained from the Fifth Affiliated Hospital of Sun Yat-sen University. Firstly, a neural network was used to detect the region of the ureteral stent, and the results of the coarse detection were completed and connected domain filtered based on the continuity of the ureteral stent in 3D space to obtain a 3D segmentation result. Secondly, the segmentation results were analyzed and detected based on the 3D morphology, and the centerline was obtained through thinning the 3D image, fitting and deriving the ureteral stent, and obtaining radial sections. Finally, the abnormal areas of the radial section were detected through polar coordinate transformation to detect the encrustation area of the ureteral stent.Results: For the detection of ureteral stent encrustations in the ureter, the algorithm's confusion matrix achieved an accuracy of 79.6% in the validation of residual stones/ureteral stent encrustations at 186 locations. Ultimately, the algorithm was validated in 222 cases, achieving a ureteral stent segmentation accuracy of 94.4% and a positive and negative judgment accuracy of 87.3%. The average detection time per case was 12 seconds.The proposed medical CT image ureteral stent wall stone detection method based on Mask-RCNN and 3D morphological analysis can effectively assist clinical doctors in diagnosing ureteral stent encrustations.

    Keywords: artificial intelligence, ureteral stent encrustation, medical imaging, Neural Network, Stone detection

    Received: 16 May 2024; Accepted: 15 Aug 2024.

    Copyright: © 2024 Hu, Yan, Liu, Qiu, Dai and Tang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Hongji Hu, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.