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Updated: Nov 27, 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

LGAN: Lung segmentation in CT scans using generative adversarial network.

Jiaxing Tan¹, Longlong Jing¹, Yumei Huo¹

¹The City University of New York, New York 10016, USA.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society

|December 5, 2020

Summary

This summary is machine-generated.

A new deep learning Generative Adversarial Network (GAN) method, LGAN, simplifies lung segmentation in CT scans. This automated approach improves efficiency and accuracy for diagnosing lung diseases.

Keywords:

Deep learning Generative Adversarial Network Lung segmentation Medical imaging analysis Thorax CT images

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Accurate lung segmentation in CT images is crucial for diagnosing various lung diseases.
Current segmentation methods often involve multiple manual steps and empirical parameter tuning, limiting efficiency and automation.

Purpose of the Study:

To develop a novel, automated lung segmentation method using deep learning.
To introduce a Generative Adversarial Network (GAN)-based schema, termed LGAN, for simplified and efficient lung segmentation in CT images.

Main Methods:

Proposed a novel Generative Adversarial Network (GAN)-based schema (LGAN) for lung segmentation.
The LGAN schema is designed for generalization across different neural network architectures.
Evaluated LGAN on the LIDC-IDRI and QIN datasets, assessing segmentation quality and shape similarity.

Main Results:

LGAN demonstrated improved performance and efficiency compared to state-of-the-art methods.
The schema achieved high segmentation quality and shape similarity metrics.
Experimental results validated the effectiveness of the proposed automated approach.

Conclusions:

The LGAN schema offers a promising tool for automated lung segmentation in CT imaging.
The simplified procedure and enhanced performance make LGAN suitable for clinical applications.
LGAN contributes to advancing AI-driven diagnostic tools in radiology.