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Related Experiment Video

Updated: Dec 11, 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

Pathological lung segmentation based on random forest combined with deep model and multi-scale superpixels.

Caixia Liu¹, Ruibin Zhao¹, Wangli Xie¹

¹Institute of EduInfo Science and Engineering, Nanjing Normal University, Nanjing, China.

Neural Processing Letters

|August 25, 2020

Summary

This summary is machine-generated.

This study presents a novel algorithm for accurate lung segmentation in pathological CT scans using random forest and deep learning. The method achieves high accuracy, aiding in pulmonary disease diagnosis and quantification.

Keywords:

Convolutional neural network Divide-and-conquer strategy Pathological lung segmentation Random forest

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Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models

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Area of Science:

Medical Imaging
Computer-Aided Diagnosis
Pulmonology

Background:

Accurate lung segmentation in pathological thoracic computed tomography (CT) scans is crucial for diagnosing pulmonary diseases.
Variability in pathological lung appearance and shape presents significant challenges for existing segmentation methods.

Purpose of the Study:

To develop and validate a novel, accurate segmentation algorithm for pathological lungs in thoracic CT images.
To improve the reliability of lung field segmentation for clinical applications in disease detection and quantification.

Main Methods:

A hybrid approach combining multi-scale superpixels, random forest (RF) classifiers, and deep convolutional networks.
Feature extraction including deep, texture, and intensity features from superpixels.
A fractional-order gray correlation approach for fusing RF classification results, followed by a divide-and-conquer strategy for segmentation refinement.

Main Results:

The algorithm achieved high segmentation accuracy on thoracic CT images with interstitial lung diseases, with an average Dice Similarity Coefficient (DSC) of 96.45% and Positive Predictive Value (PPV) of 95.07%.
Demonstrated robust performance compared to existing lung segmentation methods.
Validated its reliability for accurate lung field segmentation in pathological CT scans.

Conclusions:

The proposed algorithm offers a reliable and highly accurate solution for pathological lung segmentation in thoracic CT images.
It effectively assists radiologists in detecting pulmonary diseases and quantifying lung shape and size in clinical practice.
The method shows promise for enhancing diagnostic workflows in pulmonology.