Real-time auto-segmentation of the ureter in video sequences of gynaecological laparoscopic surgery

IF 2.3 3区医学 Q2 SURGERY

International Journal of Medical Robotics and Computer Assisted Surgery Pub Date : 2023-12-19 DOI:10.1002/rcs.2604

Zhixiang Wang, Chongdong Liu, Zhen Zhang, Yupeng Deng, Meizhu Xiao, Zhiqiang Zhang, Andre Dekker, Shuzhen Wang, Yujiang Liu, LinXue Qian, Zhenyu Zhang, Alberto Traverso, Ying Feng

{"title":"Real-time auto-segmentation of the ureter in video sequences of gynaecological laparoscopic surgery","authors":"Zhixiang Wang, Chongdong Liu, Zhen Zhang, Yupeng Deng, Meizhu Xiao, Zhiqiang Zhang, Andre Dekker, Shuzhen Wang, Yujiang Liu, LinXue Qian, Zhenyu Zhang, Alberto Traverso, Ying Feng","doi":"10.1002/rcs.2604","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Ureteral injury is common during gynaecological laparoscopic surgery. Real-time auto-segmentation can assist gynaecologists in identifying the ureter and reduce intraoperative injury risk.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>A deep learning segmentation model was crafted for ureter recognition in surgical videos, utilising 3368 frames from 11 laparoscopic surgeries. Class activation maps enhanced the model's interpretability, showing its areas. The model's clinical relevance was validated through an End-User Turing test and verified by three gynaecological surgeons.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>The model registered a Dice score of 0.86, a Hausdorff 95 distance of 22.60, and processed images in 0.008 s on average. In complex surgeries, it pinpointed the ureter's position in real-time. Fifty five surgeons across eight institutions found the model's accuracy, specificity, and sensitivity comparable to human performance. Yet, artificial intelligence experience influenced some subjective ratings.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>The model offers precise real-time ureter segmentation in laparoscopic surgery and can be a significant tool for gynaecologists to mitigate ureteral injuries.</p>\n </section>\n </div>","PeriodicalId":50311,"journal":{"name":"International Journal of Medical Robotics and Computer Assisted Surgery","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Medical Robotics and Computer Assisted Surgery","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/rcs.2604","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"SURGERY","Score":null,"Total":0}

引用次数: 0

Abstract

Background

Ureteral injury is common during gynaecological laparoscopic surgery. Real-time auto-segmentation can assist gynaecologists in identifying the ureter and reduce intraoperative injury risk.

Methods

A deep learning segmentation model was crafted for ureter recognition in surgical videos, utilising 3368 frames from 11 laparoscopic surgeries. Class activation maps enhanced the model's interpretability, showing its areas. The model's clinical relevance was validated through an End-User Turing test and verified by three gynaecological surgeons.

Results

The model registered a Dice score of 0.86, a Hausdorff 95 distance of 22.60, and processed images in 0.008 s on average. In complex surgeries, it pinpointed the ureter's position in real-time. Fifty five surgeons across eight institutions found the model's accuracy, specificity, and sensitivity comparable to human performance. Yet, artificial intelligence experience influenced some subjective ratings.

Conclusions

The model offers precise real-time ureter segmentation in laparoscopic surgery and can be a significant tool for gynaecologists to mitigate ureteral injuries.

查看原文本刊更多论文

妇科腹腔镜手术视频序列中输尿管的实时自动分割。

背景：输尿管损伤在妇科腹腔镜手术中很常见。实时自动分割可以帮助妇科医生识别输尿管，降低术中损伤风险：方法：利用 11 例腹腔镜手术中的 3368 个帧，制作了一个深度学习分割模型，用于识别手术视频中的输尿管。类激活图增强了模型的可解释性，显示了模型的区域。最终用户图灵测试验证了该模型的临床相关性，并由三位妇科外科医生进行了验证：结果：该模型的 Dice 得分为 0.86，Hausdorff 95 距离为 22.60，处理图像的平均时间为 0.008 秒。在复杂手术中，它能实时精确定位输尿管的位置。八家机构的 55 名外科医生认为，该模型的准确性、特异性和灵敏度与人类的表现相当。然而，人工智能经验影响了一些主观评价：该模型可在腹腔镜手术中实时精确地分割输尿管，是妇科医生减轻输尿管损伤的重要工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

International Journal of Medical Robotics and Computer Assisted Surgery 医学-外科

CiteScore

4.50

自引率

12.00%

发文量

131

审稿时长

6-12 weeks

期刊介绍： The International Journal of Medical Robotics and Computer Assisted Surgery provides a cross-disciplinary platform for presenting the latest developments in robotics and computer assisted technologies for medical applications. The journal publishes cutting-edge papers and expert reviews, complemented by commentaries, correspondence and conference highlights that stimulate discussion and exchange of ideas. Areas of interest include robotic surgery aids and systems, operative planning tools, medical imaging and visualisation, simulation and navigation, virtual reality, intuitive command and control systems, haptics and sensor technologies. In addition to research and surgical planning studies, the journal welcomes papers detailing clinical trials and applications of computer-assisted workflows and robotic systems in neurosurgery, urology, paediatric, orthopaedic, craniofacial, cardiovascular, thoraco-abdominal, musculoskeletal and visceral surgery. Articles providing critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies, commenting on ease of use, or addressing surgical education and training issues are also encouraged. The journal aims to foster a community that encompasses medical practitioners, researchers, and engineers and computer scientists developing robotic systems and computational tools in academic and commercial environments, with the intention of promoting and developing these exciting areas of medical technology.