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Updated: Nov 25, 2025

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

Fully automatic wound segmentation with deep convolutional neural networks.

Chuanbo Wang¹, D M Anisuzzaman¹, Victor Williamson¹

¹Big Data Analytics and Visualization Laboratory, Department of Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.

Scientific Reports

|December 15, 2020

Summary

This summary is machine-generated.

This study introduces a lightweight deep learning model for automatic wound segmentation in natural images. The novel framework accurately measures wound areas, aiding diagnosis and treatment in advanced wound care.

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Area of Science:

Medical Imaging
Computer Vision
Artificial Intelligence in Healthcare

Background:

Accurate wound diagnosis and management are critical in healthcare, with the advanced wound care market projected to exceed $22 billion by 2024.
Current reliance on manual image analysis for wound documentation is prone to inaccuracies due to varying expertise levels.
Automated wound segmentation is essential for precise area measurement and quantitative treatment parameters.

Purpose of the Study:

To develop and evaluate a novel, lightweight deep learning framework for the automatic segmentation of wound regions in natural images.
To assess the performance of the proposed model against established deep learning architectures for semantic segmentation.
To provide a readily available, efficient, and accurate tool for wound area quantification.

Main Methods:

A novel convolutional neural network framework integrating MobileNetV2 and connected component labeling was designed for wound segmentation.
A custom annotated dataset comprising 1109 foot ulcer images from 889 patients was created for training and validation.
Comprehensive experiments were conducted to compare the proposed method with various other segmentation neural networks.

Main Results:

The proposed lightweight model achieved performance comparable to deeper, more complex neural networks in wound segmentation.
The framework demonstrated effectiveness and mobility, offering accurate segmentation of wound areas.
The study successfully trained and tested deep learning models on a dedicated foot ulcer image dataset.

Conclusions:

The developed lightweight deep learning model offers an effective and computationally efficient solution for automatic wound segmentation.
This approach can improve the accuracy of wound diagnosis, management, and documentation in clinical practice.
The availability of the implementation facilitates further research and application in advanced wound care.