Incision trajectory tracing for electrosurgical navigation by CNN-based knife contacting frames extraction method

[2608.14749] Incision trajectory tracing for electrosurgical navigation by CNN-based knife contacting frames extraction method Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Image and Video Processing arXiv:2608.14749 (eess) [Submitted on 14 Aug 2026] Title: Incision trajectory tracing for electrosurgical navigation by CNN-based knife contacting frames extraction method Authors: Yu Chun Wang , Kaixu Chen , Naoto Ienaga , Yoshihiro Kuroda View a PDF of the paper titled Incision trajectory tracing for electrosurgical navigation by CNN-based knife contacting frames extraction method, by Yu Chun Wang and 3 other authors View PDF HTML (experimental) Abstract: 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. Subjects: Image and Video Processing (eess.IV) ; Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2608.14749 [eess.IV] (or arXiv:2608.14749v1 [eess.IV] for this version) https://doi.org/10.48550/arXiv.2608.14749 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yoshihiro Kuroda [ view email ] [v1] Fri, 14 Aug 2026 02:05:05 UTC (717 KB) Full-text links: Access Paper: View a PDF of the paper titled Incision trajectory tracing for electrosurgical navigation by CNN-based knife contacting frames extraction method, by Yu Chun Wang and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: eess.IV < prev | next > new | recent | 2026-08 Change to browse by: cs cs.CV eess References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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