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Surgical action detection based on path aggregation adaptive spatial network

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Abstract

Surgeon action detection plays a crucial role in computer-assisted surgery. However, due to the problems of non-rigid instrument deformation, occlusion, and less available contextual information in the surgeon action detection, these factors lead to the problem that the average accuracy of the current detection of surgical motion is very low, which needs to be solved urgently. Therefore, inspired by the application of convolutional neural networks (CNNs) can express features through learning and success in medical image detection tasks, we developed a path aggregation adaptive spatial feature pyramid network (PAAS-FPN), which combines bottom-up path enhancement and an adaptive spatial fusion mechanism. Path enhancement can use the shallow feature information of images for upward transmission. The adaptive spatial feature fusion network adds spatial granularity between deep and shallow features. In this study, the improved method was experimentally verified on the ESAD dataset and surgeon instrument detection dataset. The proposed detection method achieved the highest detection accuracy in several experiments, thereby confirming its effectiveness in surgeon action detection.

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Acknowledgements

This work was supported by grants from the Key-Area Research and Development Program of Guangdong Province (No. 2020B010165004), the National Natural Science Foundation of China (Nos. 82001905, 62172401, and 12026602), the Guangdong Natural Science Foundation (Nos. 2022A1515010439 and 2022A0505020019), the Shenzhen Key Basic Science Program (No. JCYJ2022081801802005), the National Key R&D Program (No. 2019YFC0118100), the Zhuhai Science and Technology Program (No. ZH22017002210017PWC), the Shenzhen Key Laboratory Program (No. ZDSYS201707271637577) and Academic Promotion Project of Shandong First Medical University.

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Authors and Affiliations

  1. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China

    Zhen Chao, Wenting Xu & Fucang Jia

  2. College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University & Shandong Academy of Medical Sciences, 6699 Qingdao Road, Huaiyin District, Jinan, 250117, Shandong, China

    Zhen Chao

  3. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, China

    Wenting Xu & Ruiguo Liu

  4. Department of Radiation Convergence Engineering, College of Health Science, Yonsei University, 1 Yonseidae-gil, Wonju, Gangwon, 26493, South Korea

    Hyosung Cho

  5. Pazhou Laboratory, Guangzhou, 510330, China

    Fucang Jia

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  1. Zhen Chao
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  2. Wenting Xu
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Contributions

Zhen Chao and Wenting Xu: Conceptualization, Methodology, Software, Writing-Original draft preparation.

Ruiguo Liu: Validation.

Hyosung Cho: Supervision and Writing-Reviewing.

Fucang Jia: Supervision, Writing-Reviewing and Editing.

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Correspondence to Hyosung Cho or Fucang Jia.

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Zhen Chao and Wenting Xu are regarded as co-first author.

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Chao, Z., Xu, W., Liu, R. et al. Surgical action detection based on path aggregation adaptive spatial network. Multimed Tools Appl 82, 26971–26986 (2023). https://doi.org/10.1007/s11042-023-14990-1

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