A deep learning approach for surgical instruments detection in orthopaedic surgery using transfer learning

Belayat Hossain; Shoichi Nishio; Hiranaka Takafunio; Syoji Kobashi

doi:10.1117/12.2550670

16 March 2020 A deep learning approach for surgical instruments detection in orthopaedic surgery using transfer learning

Belayat Hossain, Shoichi Nishio, Hiranaka Takafunio, Syoji Kobashi

Author Affiliations +

Proceedings Volume 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling; 113151M (2020) https://doi.org/10.1117/12.2550670
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Surgical tools detection for intraoperative surgical navigation system is essential for better coordination among surgical team in operating room. Because Orthopaedic surgery (OS) differs from laparoscopic, due to a large variety of surgical instruments and techniques making its procedures complicated. Compared to usual object detection in natural images, OS video images are confounded by inhomogeneous illumination; it is hard to directly apply existing studies that are developed for others. Additionally, acquiring Orthopaedic surgery videos is difficult due to recording of surgery videos in restricted surgical environment. Therefore, we propose a deep learning (DL) approach for surgery tools detection in OS videos by integrating knowledge of diverse representative surgery and non-surgery images of tools into the model using transfer learning (TL) and data augmentation. The proposed method has been evaluated for five surgical tools using knee surgery images following 10-fold cross validation. It shows, proposed model (mAP 62.46%) outperforms over conventional model (mAP 60%).

Citation Download Citation

Belayat Hossain, Shoichi Nishio, Hiranaka Takafunio, and Syoji Kobashi "A deep learning approach for surgical instruments detection in orthopaedic surgery using transfer learning", Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113151M (16 March 2020); https://doi.org/10.1117/12.2550670

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