Korean J Radiol.  2009 Oct;10(5):455-463. 10.3348/kjr.2009.10.5.455.

Feasibility of Automated Quantification of Regional Disease Patterns Depicted on High-Resolution Computed Tomography in Patients with Various Diffuse Lung Diseases

• Park SO
• Seo JB
• Kim N
• Park SH
• Lee YK
• Park BW
• Sung YS
• Lee Y
• Lee J
• Kang SH

• KMID: 1093960
• DOI: http://doi.org/10.3348/kjr.2009.10.5.455

Abstract

OBJECTIVE
This study was designed to develop an automated system for quantification of various regional disease patterns of diffuse lung diseases as depicted on high-resolution computed tomography (HRCT) and to compare the performance of the automated system with human readers. MATERIALS AND METHODS: A total of 600 circular regions-of-interest (ROIs), 10 pixels in diameter, were utilized. The 600 ROIs comprised 100 ROIs that represented six typical regional patterns (normal, ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation). The ROIs were used to train the automated classification system based on the use of a Support Vector Machine classifier and 37 features of texture and shape. The performance of the classification system was tested with a 5-fold cross-validation method. An automated quantification system was developed with a moving ROI in the lung area, which helped classify each pixel into six categories. A total of 92 HRCT images obtained from patients with different diseases were used to validate the quantification system. Two radiologists independently classified lung areas of the same CT images into six patterns using the manual drawing function of dedicated software. Agreement between the automated system and the readers and between the two individual readers was assessed. RESULTS: The overall accuracy of the system to classify each disease pattern based on the typical ROIs was 89%. When the quantification results were examined, the average agreement between the system and each radiologist was 52% and 49%, respectively. The agreement between the two radiologists was 67%. CONCLUSION: An automated quantification system for various regional patterns of diffuse interstitial lung diseases can be used for objective and reproducible assessment of disease severity.

Keyword

Diffuse interstitial lung disease; Computed tomography (CT), image processing; Computed tomography (CT), high resolution; Computed tomography (CT), quantitative

MeSH Terms

Feasibility Studies
Humans
Lung Diseases, Interstitial/*radiography
Observer Variation
Pattern Recognition, Automated/*methods
Radiographic Image Interpretation, Computer-Assisted
Sensitivity and Specificity
Tomography, X-Ray Computed/*methods

Figure

• 

  Fig. 1 High-resolution CT scans of chest (window level, -850 HU; width, 400 HU) are shown. On each image, three different sizes of circular (16, 32 and 64 pixel diameters) region of interest highlight features that are typical of particular condition. Normal lung parenchyma (A), ground-glass opacity (B), reticular opacity (C), honeycombing (D), emphysema (E) and consolidation (F) are shown.

• 

  Fig. 2 Quantification results are shown for use of automated system and by two readers depicted by color-coded overlay in several cases (normal, green; ground-glass opacity, yellow; reticular opacity, cyan; honeycombing, blue; emphysema, red; consolidation, pink). For cases 1 and 2, color-coded quantification results were well correlated with findings of readers except that some portions of normal vessels were considered to show reticular opacity by use of automated system. Case 3 shows discrepancy between system and readers for classification of normal, ground-glass opacity and reticular opacity. Large area classified as normal by reader 2 was considered as ground-glass opacity by reader 1 and as mixed reticular opacity and ground-glass opacity by system. Cases 4, 5 and 6 show very good agreement between use of system and readers for quantification of honeycombing, emphysema and consolidation.

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