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Deep learning-based classification and segmentation for scalpels

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

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%, respectively, 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.

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Acknowledgements

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).

Author information

Authors and Affiliations

  1. Medical Robotics Laboratory, School of Automation, Beijing University of Posts and Telecommunications, Beijing, China

    Baiquan Su, Qingqian Zhang, Yi Gong, Han Li, Zehao Wang & Shi Yu

  2. Chinese Institute of Electronics, Beijing, China

    Wei Xiu & Yang Gao

  3. Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China

    Lixin Xu & Jie Tang

  4. Brigham and Women’s Hospital, Harvard Medical School, Boston, USA

    Yida David Hu

  5. Gastroenterology Department, Peking University Third Hospital, Beijing, China

    Wei Yao

  6. School of Mechanical Engineering and Automation, Beihang University, Beijing, China

    Junchen Wang

  7. School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China

    Changsheng Li

  8. Department of Periodontology, National Stomatological Center, Peking University School and Hospital of Stomatology, Beijing, China

    Li Gao

  9. National Clinical Research Center for Oral Diseases, Beijing, China

    Li Gao

  10. National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, China

    Li Gao

  11. Beijing Key Laboratory of Digital Stomatology, Beijing, China

    Li Gao

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  1. Baiquan Su
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Correspondence to Jie Tang or Li Gao.

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The study was approved by the Ethics Committee of Xuanwu Hospital.

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Baiquan Su, Qingqian Zhang and Yi Gong are the co-first authors of this paper.

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Su, B., Zhang, Q., Gong, Y. et al. Deep learning-based classification and segmentation for scalpels. Int J CARS 18, 855–864 (2023). https://doi.org/10.1007/s11548-022-02825-7

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