ArticleSurgical endoscopy2021
Computer-assisted real-time automatic prostate segmentation during TaTME: a single-center feasibility study.
Article in Surgical endoscopy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Semantic segmentation deep learning model boosts surgeons' organ recognition in minimally invasive hysterectomy - a prospective multi-center reader performance study using pre-selected video clips.International journal of surgery (London, England) · 2026Article
- Deep learning-assisted near-real-time identification of the inferior mesenteric artery and vein during laparoscopic rectal resection: a single-center proof-of-concept study.Frontiers in medicine · 2026Article
- Laparoscopic near-infrared hyperspectral imaging system for identifying living porcine nerves and unexposed arteries.Biomedical optics express · 2025Article
- Deep learning-based vessel and nerve recognition model for lateral lymph node dissection: a retrospective feasibility study.Langenbeck's archives of surgery · 2025Article
- Feasibility of Simultaneous Artificial Intelligence-Assisted and NIR Fluorescence Navigation for Anatomical Recognition in Laparoscopic Colorectal Surgery.Journal of fluorescence · 2025Article
- Enhancing efficient deep learning models with multimodal, multi-teacher insights for medical image segmentation.Scientific reports · 2025Article
- Artificial intelligence-enhanced navigation for nerve recognition and surgical education in laparoscopic colorectal surgery.Surgical endoscopy · 2025Article
- Anatomical recognition artificial intelligence for identifying the recurrent laryngeal nerve during endoscopic thyroid surgery: A single-center feasibility study.Laryngoscope investigative otolaryngology · 2024Article
- Deep learning-based automatic bleeding recognition during liver resection in laparoscopic hepatectomy.Surgical endoscopy · 2024Article
- Artificial intelligence assisted operative anatomy recognition in endoscopic pituitary surgery.NPJ digital medicine · 2024Article
- Deep learning based clinical target volumes contouring for prostate cancer: Easy and efficient application.Journal of applied clinical medical physics · 2024Article
- Real-time segmentation of biliary structure in pure laparoscopic donor hepatectomy.Scientific reports · 2024Article
- Deep learning-based vessel automatic recognition for laparoscopic right hemicolectomy.Surgical endoscopy · 2024Article
- Real-time detection of the recurrent laryngeal nerve in thoracoscopic esophagectomy using artificial intelligence.Surgical endoscopy · 2022Article
- Essential updates 2020/2021: Current topics of simulation and navigation in hepatectomy.Annals of gastroenterological surgery · 2022Review
- Artificial intelligence-based computer vision in surgery: Recent advances and future perspectives.Annals of gastroenterological surgery · 2022Review
- Development and Validation of a 3-Dimensional Convolutional Neural Network for Automatic Surgical Skill Assessment Based on Spatiotemporal Video Analysis.JAMA network open · 2021Article
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8 authors.
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Abstract
backgroundUrethral injuries (UIs) are significant complications pertaining to transanal total mesorectal excision (TaTME). It is important for surgeons to identify the prostate during TaTME to prevent UI occurrence; intraoperative image navigation could be considered useful in this regard. This study aims at developing a deep learning model for real-time automatic prostate segmentation based on intraoperative video during TaTME. The proposed model's performance has been evaluated.
methodsThis was a single-institution retrospective feasibility study. Semantic segmentation of the prostate area was performed using a convolutional neural network (CNN)-based approach. DeepLab v3 plus was utilized as the CNN model for the semantic segmentation task. The Dice coefficient (DC), which is calculated based on the overlapping area between the ground truth and predicted area, was utilized as an evaluation metric for the proposed model.
resultsFive hundred prostate images were randomly extracted from 17 TaTME videos, and the prostate area was manually annotated on each image. Fivefold cross-validation tests were performed, and as observed, the average DC value equaled 0.71 ± 0.04, the maximum value being 0.77. Additionally, the model operated at 11 fps, which provides acceptable real-time performance.
conclusionsTo the best of the authors' knowledge, this is the first effort toward realization of computer-assisted TaTME, and results obtained in this study suggest that the proposed deep learning model can be utilized for real-time automatic prostate segmentation. In future endeavors, the accuracy and performance of the proposed model will be improved to enable its use in practical applications, and its capability to reduce UI risks during TaTME will be verified.
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