Real-Time Surgical Instrument Segmentation Analysis Using YOLOv8 With ByteTrack for Laparoscopic Surgery

Nyi Myo, Nyi; Boonkong, Apiwat; Khampitak, Kovit; Hormdee, Daranee

doi:10.1109/access.2024.3412780

IEEE Access

2024

DOI: 10.1109/access.2024.3412780

|View full text |Cite

Real-Time Surgical Instrument Segmentation Analysis Using YOLOv8 With ByteTrack for Laparoscopic Surgery

Nyi Nyi Myo,

Apiwat Boonkong,

Kovit Khampitak

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Preprint1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Optimizing Intraoperative AI: Evaluation of YOLOv8 for Real-Time Recognition of Robotic and Laparoscopic Instruments.

Frey,

Facente,

Wei

et al. 2024

Preprint

View full text Add to dashboard Cite

The accurate recognition of surgical instruments is essential for the advancement of intraoperative artificial intelligence (AI) systems. In this study, we assessed the YOLOv8 model’s efficacy in identifying robotic and laparoscopic instruments in robot-assisted abdominal surgeries. Specifically, we evaluated its ability to detect, classify, and segment seven different types of surgical instruments. A diverse dataset was compiled from four public and private sources, encompassing over 7,400 frames and 17,175 annotations that represent a variety of surgical contexts and instruments. YOLOv8 was trained and tested on these datasets, achieving a mean average precision of 0.77 for binary detection and 0.72 for multi-instrument classification. Optimal performance was observed when the training set of a specific instrument reached 1300 instances. The model also demonstrated excellent segmentation accuracy, achieving a mean Dice score of 0.91 and a mean intersection over union of 0.86, with Monopolar Curved Scissors yielding the highest accuracy. Notably, YOLOv8 exhibited superior recognition performance for robotic instruments compared to laparoscopic tools, a difference likely attributed to the greater representation of robotic instruments in the training set. Furthermore, the model's rapid inference speed of 1.12 milliseconds per frame highlights its suitability for real-time clinical applications. These findings confirm YOLOv8’s potential for precise and efficient recognition of surgical instruments using a comprehensive multi-source dataset.

show abstract

Optimizing Intraoperative AI: Evaluation of YOLOv8 for Real-Time Recognition of Robotic and Laparoscopic Instruments.

Frey,

Facente,

Wei

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Real-Time Surgical Instrument Segmentation Analysis Using YOLOv8 With ByteTrack for Laparoscopic Surgery

Cited by 1 publication

References 27 publications

Optimizing Intraoperative AI: Evaluation of YOLOv8 for Real-Time Recognition of Robotic and Laparoscopic Instruments.

Optimizing Intraoperative AI: Evaluation of YOLOv8 for Real-Time Recognition of Robotic and Laparoscopic Instruments.

Contact Info

Product

Resources

About