发表机构
University of Tsukuba; Center for Computational Sciences, University of Tsukuba; Institute of Systems and Information Engineering, University of Tsukuba（筑波大学; 筑波大学计算科学中心; 筑波大学系统与信息工程研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于CNN的手术器械接触帧提取方法，用于电外科导航的切口轨迹追踪，可准确区分电刀、超声刀与组织的接触，误差较传统方法降低超2.5倍，且避免了轨迹缺失问题。
AI 中文摘要
背景与目的：图像引导手术导航因能识别皮下目标和关键结构而得到积极研究，但其需要切口轨迹在手术中动态更新术前三维模型；本研究的创新点在于利用卷积神经网络（CNN）区分电外科器械是否接触组织的热特征，并提取器械接触帧以形成符合手术要求的切口轨迹。方法：本研究首先验证了CNN可分别对电刀和超声刀操作的热图像进行分类，且通过连接CNN预测为接触帧的热强度质心得到的切口轨迹，其准确性更高。结果：使用电刀时，CNN识别的准确率高达97.2%，与传统方法相比，切口轨迹预测的误差减少了2.5倍以上；使用另一款电外科器械超声刀时，准确率高达93.7%。结论：本研究证实了CNN区分电外科器械是否接触组织的可能性，所提方法不仅克服了卷积长短期记忆法常见的轨迹缺失问题，还在精度上实现了显著提升且限制更少。
英文摘要
Background and Objective: Image-guided surgical navigation has been actively studied because of its advantage of identifying subsurface targets and critical structures, whereas it requires incision trajectories to update the preoperative three-dimensional model dynamically during the surgery. The novelty of this study is the thermal feature distinguishment of whether the electric tools contacting the tissue by Convolutional Neural Network (CNN), and the extraction of the knife contacting frames, to form incision trajectories which can meet with the requirement during the surgery. Methods: This study firstly verified that CNN can classify the thermal images of electric knife and ultrasonic cutter operations separately, and can raise the accuracy of the incision trajectories derived from the connection of the thermal intensity centroid of the frames predicted by CNN as contacting. Results: Our results obtained by employing the electric knife not only reveal a remarkably high accuracy 97.2 % in CNNs identification, but also can achieve an error reduction as high as more than 2.5 times of the incision trajectory prediction as compared to those proceeded in the conventional method. Besides electric knife, the results obtained by employing another electric tool, ultrasonic cutter, reveal a high accuracy up to 93.7 %. Conclusion: In this study, we ensured the possibility of CNN in distinguishing electric tools contacting with the tissue, and confirmed that the proposed method has not only overcome the problem of missing trajectories which usually occurs in the convolutional long-short term memory method but also achieved a remarkable improvement of the accuracy with less limitation.
