Development and clinical validation of a deep learning-based knee CT image segmentation method for robotic-assisted total knee arthroplasty

IF 2.3 3区医学 Q2 SURGERY

International Journal of Medical Robotics and Computer Assisted Surgery Pub Date : 2024-07-12 DOI:10.1002/rcs.2664

Xingyu Liu, Songlin Li, Xiongfei Zou, Xi Chen, Hongjun Xu, Yang Yu, Zhao Gu, Dong Liu, Runchao Li, Yaojiong Wu, Guangzhi Wang, Hongen Liao, Wenwei Qian, Yiling Zhang

{"title":"Development and clinical validation of a deep learning-based knee CT image segmentation method for robotic-assisted total knee arthroplasty","authors":"Xingyu Liu, Songlin Li, Xiongfei Zou, Xi Chen, Hongjun Xu, Yang Yu, Zhao Gu, Dong Liu, Runchao Li, Yaojiong Wu, Guangzhi Wang, Hongen Liao, Wenwei Qian, Yiling Zhang","doi":"10.1002/rcs.2664","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>This study aimed to develop a novel deep convolutional neural network called Dual-path Double Attention Transformer (DDA-Transformer) designed to achieve precise and fast knee joint CT image segmentation and to validate it in robotic-assisted total knee arthroplasty (TKA).</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>The femoral, tibial, patellar, and fibular segmentation performance and speed were evaluated and the accuracy of component sizing, bone resection and alignment of the robotic-assisted TKA system constructed using this deep learning network was clinically validated.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Overall, DDA-Transformer outperformed six other networks in terms of the Dice coefficient, intersection over union, average surface distance, and Hausdorff distance. DDA-Transformer exhibited significantly faster segmentation speeds than nnUnet, TransUnet and 3D-Unet (<i>p</i> < 0.01). Furthermore, the robotic-assisted TKA system outperforms the manual group in surgical accuracy.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>DDA-Transformer exhibited significantly improved accuracy and robustness in knee joint segmentation, and this convenient and stable knee joint CT image segmentation network significantly improved the accuracy of the TKA procedure.</p>\n </section>\n </div>","PeriodicalId":50311,"journal":{"name":"International Journal of Medical Robotics and Computer Assisted Surgery","volume":"20 4","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-07-12","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.2664","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

This study aimed to develop a novel deep convolutional neural network called Dual-path Double Attention Transformer (DDA-Transformer) designed to achieve precise and fast knee joint CT image segmentation and to validate it in robotic-assisted total knee arthroplasty (TKA).

Methods

The femoral, tibial, patellar, and fibular segmentation performance and speed were evaluated and the accuracy of component sizing, bone resection and alignment of the robotic-assisted TKA system constructed using this deep learning network was clinically validated.

Results

Overall, DDA-Transformer outperformed six other networks in terms of the Dice coefficient, intersection over union, average surface distance, and Hausdorff distance. DDA-Transformer exhibited significantly faster segmentation speeds than nnUnet, TransUnet and 3D-Unet (p < 0.01). Furthermore, the robotic-assisted TKA system outperforms the manual group in surgical accuracy.

Conclusions

DDA-Transformer exhibited significantly improved accuracy and robustness in knee joint segmentation, and this convenient and stable knee joint CT image segmentation network significantly improved the accuracy of the TKA procedure.

查看原文本刊更多论文

为机器人辅助全膝关节置换术开发基于深度学习的膝关节 CT 图像分割方法并进行临床验证。

研究背景本研究旨在开发一种名为双路径双注意变换器（DDA-Transformer）的新型深度卷积神经网络，以实现精确、快速的膝关节 CT 图像分割，并在机器人辅助全膝关节置换术（TKA）中进行验证：评估了股骨、胫骨、髌骨和腓骨的分割性能和速度，并对使用该深度学习网络构建的机器人辅助全膝关节置换术（TKA）系统的组件尺寸、骨切除和对位的准确性进行了临床验证：总体而言，DDA-Transformer在骰子系数、交集大于联合、平均表面距离和豪斯多夫距离方面的表现优于其他六个网络。DDA-Transformer 的分割速度明显快于 nnUnet、TransUnet 和 3D-Unet （p 结论：DDA-Transformer 的分割速度明显快于 nnUnet、TransUnet 和 3D-Unet ：DDA-Transformer 在膝关节分割方面的准确性和鲁棒性都有明显提高，这种方便、稳定的膝关节 CT 图像分割网络显著提高了 TKA 手术的准确性。

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.