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. 2011 Feb 21;56(4):1139-53.
doi: 10.1088/0031-9155/56/4/016. Epub 2011 Jan 25.

Computer-aided detection of early interstitial lung diseases using low-dose CT images

Sang Cheol Park  1 , Jun Tan, Xingwei Wang, Dror Lederman, Joseph K Leader, Soo Hyung Kim, Bin Zheng
Affiliations

Computer-aided detection of early interstitial lung diseases using low-dose CT images

Sang Cheol Park et al. Phys Med Biol. .

Abstract

This study aims to develop a new computer-aided detection (CAD) scheme to detect early interstitial lung disease (ILD) using low-dose computed tomography (CT) examinations. The CAD scheme classifies each pixel depicted on the segmented lung areas into positive or negative groups for ILD using a mesh-grid-based region growth method and a multi-feature-based artificial neural network (ANN). A genetic algorithm was applied to select optimal image features and the ANN structure. In testing each CT examination, only pixels selected by the mesh-grid region growth method were analyzed and classified by the ANN to improve computational efficiency. All unselected pixels were classified as negative for ILD. After classifying all pixels into the positive and negative groups, CAD computed a detection score based on the ratio of the number of positive pixels to all pixels in the segmented lung areas, which indicates the likelihood of the test case being positive for ILD. When applying to an independent testing dataset of 15 positive and 15 negative cases, the CAD scheme yielded the area under receiver operating characteristic curve (AUC = 0.884 ± 0.064) and 80.0% sensitivity at 85.7% specificity. The results demonstrated the feasibility of applying the CAD scheme to automatically detect early ILD using low-dose CT examinations.

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Figures

Figure 1
Figure 1
The general processing steps of the CAD scheme to classify each pixel into either negative or positive group for ILD.
Figure 2
Figure 2
Examples of partial detection results using the CAD scheme. Two images in the first column and images in the second column are the original images selected from different CT scans for demonstration and the images marked with CAD-generated results (including the boundary contour of the segmented lung areas and identified ILD lesions), respectively. The white circles in the result images indicate false positives because the tissue around vessel is similar to the ILD texture pattern.
Figure 3
Figure 3
A ROC-type performance curve for applying the CAD scheme to 30 testing CT examinations.
Figure 4
Figure 4
Distributions of detection score calculated by the CAD scheme applied to negative (normal) and positive (abnormal) CT examinations.
See this image and copyright information in PMC

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