机构：[1]Medical Robotics Laboratory, School of Automation, Beijing University of Posts and Telecommunications, Beijing, China[2]Chinese Institute of Electronics, Beijing, China[3]Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China神经科系统神经外科首都医科大学宣武医院[4]Brigham and Women’s Hospital, Harvard Medical School, Boston, USA[5]Gastroenterology Department, Peking University Third Hospital, Beijing, China[6]School of Mechanical Engineering and Automation, Beihang University, Beijing, China[7]School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China[8]Department of Periodontology, National Stomatological Center, Peking University School and Hospital of Stomatology, Beijing, China[9]National Clinical Research Center for Oral Diseases, Beijing, China[10]National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, China[11]Beijing Key Laboratory of Digital Stomatology, Beijing, China
Purpose Scalpels are typical tools used for cutting in surgery, and the surgical tray is one of the locations where the scalpel is present during surgery. However, there is no known method for the classification and segmentation of multiple types of scalpels. This paper presents a dataset of multiple types of scalpels and a classification and segmentation method that can be applied as a first step for validating segmentation of scalpels and further applications can include identifying scalpels from other tools in different clinical scenarios.Methods The proposed scalpel dataset contains 6400 images with labeled information of 10 types of scalpels, and a classification and segmentation model for multiple types of scalpels is obtained by training the dataset based on Mask R-CNN. The article concludes with an analysis and evaluation of the network performance, verifying the feasibility of the work.Results A multi-type scalpel dataset was established, and the classification and segmentation models of multi-type scalpel were obtained by training the Mask R-CNN. The average accuracy and average recall reached 94.19% and 96.61%, respec-tively, in the classification task and 93.30% and 95.14%, respectively, in the segmentation task. Conclusion The first scalpel dataset is created covering multiple types of scalpels. And the classification and segmentation of multiple types of scalpels are realized for the first time. This study achieves the classification and segmentation of scalpels in a surgical tray scene, providing a potential solution for scalpel recognition, localization and tracking.
基金：
We appreciated the financial support of the
National Natural Science Foundation of China (Grant Nos. 62273055,91748103, 61573208) and Beijing Natural Science Foundation (Grant
No. Z170001).
语种：
外文
被引次数：
WOS：
PubmedID：
中科院(CAS)分区：
出版当年[2022]版：
大类|3 区工程技术
小类|3 区工程：生物医学3 区核医学3 区外科
最新[2023]版：
大类|3 区医学
小类|3 区工程：生物医学3 区核医学3 区外科
JCR分区：
出版当年[2021]版：
Q2RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGINGQ2SURGERYQ3ENGINEERING, BIOMEDICAL
最新[2023]版：
Q2RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGINGQ2SURGERYQ3ENGINEERING, BIOMEDICAL
第一作者机构：[1]Medical Robotics Laboratory, School of Automation, Beijing University of Posts and Telecommunications, Beijing, China
共同第一作者：
通讯作者：
通讯机构：[8]Department of Periodontology, National Stomatological Center, Peking University School and Hospital of Stomatology, Beijing, China[9]National Clinical Research Center for Oral Diseases, Beijing, China[10]National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, China[11]Beijing Key Laboratory of Digital Stomatology, Beijing, China
推荐引用方式(GB/T 7714)：
Su Baiquan,Zhang Qingqian,Gong Yi,et al.Deep learning-based classification and segmentation for scalpels[J].INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY.2023,18(5):855-864.doi:10.1007/s11548-022-02825-7.
APA：
Su, Baiquan,Zhang, Qingqian,Gong, Yi,Xiu, Wei,Gao, Yang...&Gao, Li.(2023).Deep learning-based classification and segmentation for scalpels.INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY,18,(5)
MLA：
Su, Baiquan,et al."Deep learning-based classification and segmentation for scalpels".INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 18..5(2023):855-864
