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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Deep learning-based classification and segmentation for scalpels.

Baiquan Su¹, Qingqian Zhang¹, Yi Gong¹

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

International Journal of Computer Assisted Radiology and Surgery

|January 5, 2023

Summary

This summary is machine-generated.

This study introduces the first dataset for classifying and segmenting multiple types of scalpels. The developed Mask R-CNN model achieves high accuracy for scalpel recognition in surgical settings.

Keywords:

Classification Dataset Deep learning Scalpel Segmentation

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Area of Science:

Medical imaging
Computer vision
Surgical technology

Background:

Scalpels are essential surgical tools, commonly found in surgical trays.
Current methods lack the ability to classify and segment multiple types of scalpels.
Automated recognition of surgical instruments is crucial for various clinical applications.

Purpose of the Study:

To create a comprehensive dataset of multiple scalpel types.
To develop a robust method for classifying and segmenting various scalpels.
To lay the groundwork for automated scalpel identification and tracking in surgical environments.

Main Methods:

A dataset of 6400 images featuring 10 types of scalpels was curated.
The Mask R-CNN model was trained on this dataset for classification and segmentation tasks.
Network performance was rigorously analyzed and evaluated.

Main Results:

A novel multi-type scalpel dataset was successfully established.
Classification achieved an average accuracy of 94.19% and average recall of 96.61%.
Segmentation yielded an average accuracy of 93.30% and average recall of 95.14%.

Conclusions:

This research presents the first dataset and method for multi-type scalpel classification and segmentation.
The developed model demonstrates high performance in recognizing scalpels within a surgical tray.
This work offers a potential solution for real-time scalpel recognition, localization, and tracking.