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Related Experiment Video

Updated: Aug 16, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

An Automatic Segmentation Method for Lung Tumor Based on Improved Region Growing Algorithm.

Monan Wang¹, Donghui Li¹

¹Mechanical & Power Engineering College, Harbin University of Science and Technology, Harbin 150080, China.

Diagnostics (Basel, Switzerland)

|December 23, 2022

Summary

This summary is machine-generated.

This study introduces an improved region growing algorithm for automatic lung tumor segmentation in CT scans. The method enhances accuracy and outperforms existing techniques, aiding clinical decisions.

Keywords:

automatically update thresholds growth restriction conditions improved region growing algorithm lung tumor segmentation

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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Area of Science:

Medical Image Processing
Radiology
Computer-Aided Diagnosis

Background:

Accurate lung tumor segmentation is crucial for effective surgical planning and treatment.
Existing segmentation algorithms often face challenges in achieving optimal performance.
Computer-aided segmentation offers significant potential to assist clinicians.

Purpose of the Study:

To develop an automatic lung tumor segmentation method with improved accuracy.
To enhance the segmentation performance of computer-aided diagnosis systems for lung tumors.
To provide a reliable basis for clinical treatment decisions through precise segmentation.

Main Methods:

An improved region growing algorithm incorporating prior lung tumor information for automatic seed point selection.
Implementation of a seed point expansion mechanism and an automatic threshold update mechanism.
Combination of multiple segmentation results to derive the final segmentation output.

Main Results:

The proposed method achieved an average Dice coefficient of 0.936 ± 0.027 and an average Jaccard distance of 0.114 ± 0.049 on the LIDC-IDRI dataset.
Demonstrated superior performance compared to four other popular segmentation methods, with significant improvements in Dice coefficients.
Successfully validated through 10 experiments on a diverse lung image dataset.

Conclusions:

The developed method effectively automates lung tumor segmentation in CT slices.
The proposed algorithm exhibits suitable and competitive segmentation performance for clinical applications.
This advancement contributes to more reliable computer-aided diagnosis in oncology.