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A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
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Computer-assisted real-time automatic prostate segmentation during TaTME: a single-center feasibility study.

Daichi Kitaguchi1,2,3, Nobuyoshi Takeshita4,5, Hiroki Matsuzaki1

  • 1Surgical Device Innovation Office, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.

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Summary

This study developed a deep learning model for real-time prostate segmentation during transanal total mesorectal excision (TaTME) surgery. The model achieved an average Dice coefficient of 0.71, demonstrating its potential to reduce urethral injuries.

Keywords:
Convolutional neural networkDeep learningProstate recognitionSemantic segmentationTaTMEUrethral injury

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Area of Science:

  • Medical Imaging
  • Surgical Technology
  • Artificial Intelligence in Medicine

Background:

  • Urethral injuries (UIs) are a significant complication of transanal total mesorectal excision (TaTME).
  • Accurate identification of the prostate during TaTME is crucial for preventing UIs.
  • Intraoperative image navigation may aid surgeons in prostate identification.

Purpose of the Study:

  • To develop a deep learning model for real-time automatic prostate segmentation during TaTME.
  • To evaluate the performance of the proposed model for intraoperative use.

Main Methods:

  • A retrospective feasibility study using a convolutional neural network (CNN), specifically DeepLab v3 plus, for semantic segmentation of the prostate.
  • Evaluation metric: Dice coefficient (DC) to measure overlap between ground truth and predicted prostate areas.
  • Model trained and tested on 500 prostate images from 17 TaTME videos with fivefold cross-validation.

Main Results:

  • The deep learning model achieved an average Dice coefficient of 0.71 ± 0.04 (maximum 0.77).
  • The model demonstrated real-time performance, operating at 11 frames per second (fps).

Conclusions:

  • This represents the first computer-assisted approach for TaTME using deep learning for prostate segmentation.
  • The proposed model shows potential for real-time automatic prostate segmentation, aiding surgeons and potentially reducing UI risks.
  • Future work will focus on improving model accuracy and verifying its clinical utility in reducing urethral injuries during TaTME.