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http://hdl.handle.net/10397/106380
Title: | A learning approach for suture thread detection with feature enhancement and segmentation for 3-D shape reconstruction | Authors: | Lu, B Yu, XB Lai, JW Huang, KC Chan, KCC Chu, HK |
Issue Date: | Apr-2020 | Source: | IEEE transactions on automation science and engineering, Apr. 2020, v. 17, no. 2, p. 858-870 | Abstract: | A vision-based system presents one of the most reliable methods for achieving an automated robot-assisted manipulation associated with surgical knot tying. However, some challenges in suture thread detection and automated suture thread grasping significantly hinder the realization of a fully automated surgical knot tying. In this article, we propose a novel algorithm that can be used for computing the 3-D coordinates of a suture thread in knot tying. After proper training with our data set, we built a deep-learning model for accurately locating the suture's tip. By applying a Hessian-based filter with multiscale parameters, the environmental noises can be eliminated while preserving the suture thread information. A multistencils fast marching method was then employed to segment the suture thread, and a precise stereomatching algorithm was implemented to compute the 3-D coordinates of this thread. Experiments associated with the precision of the deep-learning model, the robustness of the 2-D segmentation approach, and the overall accuracy of 3-D coordinate computation of the suture thread were conducted in various scenarios, and the results quantitatively validate the feasibility and reliability of the entire scheme for automated 3-D shape reconstruction. | Keywords: | 3-D coordinates computation Stereovision Surgical robot Suture thread detection |
Publisher: | Institute of Electrical and Electronics Engineers | Journal: | IEEE transactions on automation science and engineering | ISSN: | 1545-5955 | EISSN: | 1558-3783 | DOI: | 10.1109/TASE.2019.2950005 | Rights: | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The following publication B. Lu, X. B. Yu, J. W. Lai, K. C. Huang, K. C. C. Chan and H. K. Chu, "A Learning Approach for Suture Thread Detection With Feature Enhancement and Segmentation for 3-D Shape Reconstruction," in IEEE Transactions on Automation Science and Engineering, vol. 17, no. 2, pp. 858-870, April 2020 is available at https://doi.org/10.1109/TASE.2019.2950005. |
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Lu_Learning_Approach_Suture.pdf | Pre-Published version | 5.59 MB | Adobe PDF | View/Open |
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