In this study, a system is developed to highlight surgical instruments used in total knee arthroplasty using object detection technology and a projector. In addition, thi...
Abstract:
Currently, there is a shortage of nurses worldwide. In total knee arthroplasty, many surgical instruments of similar shapes and sizes are available. As a result, medical ...Show MoreMetadata
Abstract:
Currently, there is a shortage of nurses worldwide. In total knee arthroplasty, many surgical instruments of similar shapes and sizes are available. As a result, medical accidents have occurred due to errors in the selection of these instruments. To solve these problems, several surgical instrument detection systems using object detection methods have been developed. However, these studies do not mention how to display the detection results. It is inefficient for scrub nurses to look at the display to check the detection results, and mistakes can occur. Therefore, in this study, we have developed a system that detects surgical instruments and uses the detection results to highlight the instruments using a projector like projection mapping. This study also describes a method for creating training data and an inference method to reduce the additional training time required when the number of detection targets is increased. In the experiments, we verify the effectiveness of the calibration system for the camera and projector. In addition, object detection is performed on a surgical instrument to verify the accuracy of the system. The results showed that the error after calibration is 0.15 cm. The IoU and F-measure of object detection are 0.911 and 0.958, respectively, indicating the effectiveness of the system.
In this study, a system is developed to highlight surgical instruments used in total knee arthroplasty using object detection technology and a projector. In addition, thi...
Published in: IEEE Access ( Volume: 12)
Funding Agency:
Graduate School Engineering, University of Fukui, Fukui, Japan
Ryusei Kasai (Student Member, IEEE) received the B.E. degree in mechanical and system engineering from the University of Fukui, Fukui, Japan, in 2023, where he is currently pursuing the M.E. degree.
His research interests include machine learning and image processing.
Ryusei Kasai (Student Member, IEEE) received the B.E. degree in mechanical and system engineering from the University of Fukui, Fukui, Japan, in 2023, where he is currently pursuing the M.E. degree.
His research interests include machine learning and image processing.View more
Graduate School Engineering, University of Fukui, Fukui, Japan
Kouki Nagamune (Senior Member, IEEE) received the B.E., M.E., Ph.D. degrees in computer engineering from Himeji Institute of Technology, Hyogo, Japan, in 1999, 2001, and 2004, respectively.
He was a Researcher and a Lecturer with the Department of Orthopaedic Surgery, Graduate School of Medicine, Kobe University, Hyogo, from 2004 to 2005 and from 2005 to 2007, respectively, and a Lecturer with the Department of Human and A...Show More
Kouki Nagamune (Senior Member, IEEE) received the B.E., M.E., Ph.D. degrees in computer engineering from Himeji Institute of Technology, Hyogo, Japan, in 1999, 2001, and 2004, respectively.
He was a Researcher and a Lecturer with the Department of Orthopaedic Surgery, Graduate School of Medicine, Kobe University, Hyogo, from 2004 to 2005 and from 2005 to 2007, respectively, and a Lecturer with the Department of Human and A...View more
Contents Graduate School Engineering, University of Fukui, Fukui, Japan
Ryusei Kasai (Student Member, IEEE) received the B.E. degree in mechanical and system engineering from the University of Fukui, Fukui, Japan, in 2023, where he is currently pursuing the M.E. degree.
His research interests include machine learning and image processing.
Ryusei Kasai (Student Member, IEEE) received the B.E. degree in mechanical and system engineering from the University of Fukui, Fukui, Japan, in 2023, where he is currently pursuing the M.E. degree.
His research interests include machine learning and image processing.View more
Graduate School Engineering, University of Fukui, Fukui, Japan
Kouki Nagamune (Senior Member, IEEE) received the B.E., M.E., Ph.D. degrees in computer engineering from Himeji Institute of Technology, Hyogo, Japan, in 1999, 2001, and 2004, respectively.
He was a Researcher and a Lecturer with the Department of Orthopaedic Surgery, Graduate School of Medicine, Kobe University, Hyogo, from 2004 to 2005 and from 2005 to 2007, respectively, and a Lecturer with the Department of Human and Artificial Intelligent System, Graduate School of Engineering, University of Fukui, Fukui, Japan, from 2007 to 2010, where he has been an Associate Professor, since 2010. His research interests include computer-aided surgery, computer-aided diagnosis, signal processing, and fuzzy logic.
Dr. Nagamune is a member of Japanese Journal of Clinical Biomechanics and Japanese Society for Medical and Biological Engineering.
Kouki Nagamune (Senior Member, IEEE) received the B.E., M.E., Ph.D. degrees in computer engineering from Himeji Institute of Technology, Hyogo, Japan, in 1999, 2001, and 2004, respectively.
He was a Researcher and a Lecturer with the Department of Orthopaedic Surgery, Graduate School of Medicine, Kobe University, Hyogo, from 2004 to 2005 and from 2005 to 2007, respectively, and a Lecturer with the Department of Human and Artificial Intelligent System, Graduate School of Engineering, University of Fukui, Fukui, Japan, from 2007 to 2010, where he has been an Associate Professor, since 2010. His research interests include computer-aided surgery, computer-aided diagnosis, signal processing, and fuzzy logic.
Dr. Nagamune is a member of Japanese Journal of Clinical Biomechanics and Japanese Society for Medical and Biological Engineering.View more