DBH-YOLO: a surgical instrument detection method based on feature separation in laparoscopic surgery.

Autor: Pan X; School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, GuoDu, Xi'an, 710121, Shaanxi, China. panxiaoying@xupt.edu.cn., Bi M; School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, GuoDu, Xi'an, 710121, Shaanxi, China., Wang H; School of Software, Northwestern Polytechnical University, Xi'an, 710072, China., Ma C; School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, GuoDu, Xi'an, 710121, Shaanxi, China., He X; Department of General Surgery, Tangdu Hospital, Air Force Medical University, Xi'an, 710038, Shaanxi, China.
Jazyk: angličtina
Zdroj: International journal of computer assisted radiology and surgery [Int J Comput Assist Radiol Surg] 2024 Nov; Vol. 19 (11), pp. 2215-2225. Date of Electronic Publication: 2024 Apr 13.
DOI: 10.1007/s11548-024-03115-0
Abstrakt: Purpose: Accurately locating and analysing surgical instruments in laparoscopic surgical videos can assist doctors in postoperative quality assessment. This can provide patients with more scientific and rational solutions for healing surgical complications. Therefore, we propose an end-to-end algorithm for the detection of surgical instruments.
Methods: Dual-Branched Head (DBH) and Overall Intersection over Union Loss (OIoU Loss) are introduced to solve the problem of inaccurate surgical instrument detection, both in terms of localization and classification. An effective method (DBHYOLO) for the detection for laparoscopic surgery in complex scenarios is proposed. This study manually annotates a new laparoscopic gastric cancer resection surgical instrument location dataset LGIL, which provides a better validation platform for surgical instrument detection methods.
Results: The proposed method's performance was tested using the m2cai16-tool-locations, LGIL, and Onyeogulu datasets. The mean Average Precision (mAP) values obtained were 96.8%, 95.6%, and 98.4%, respectively, which were higher than the other classical models compared. The improved model is more effective than the benchmark network in distinguishing between surgical instrument classes with high similarity and avoiding too many missed detection cases.
Conclusions: In this paper, the problem of inaccurate detection of surgical instruments is addressed from two different perspectives: classification and localization. And the experimental results on three representative datasets verify the performance of DBH-YOLO. It is shown that this method has a good generalization capability.
(© 2024. CARS.)
Databáze: MEDLINE
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