신세포암에서 미세혈관밀도 정량화를 위한 영상분석
Image Analysis Quantifying Microvessel Density in Renal Cell Carcinoma
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초록
Objective: The most widely used method for quantifying new blood vessel growth in tumor angiogenesis is the determination of microvessel density, which is reported to be associated with tumor progression and metastasis, and a prognostic indicator of patient outcome. In this study, we propose a method for the determination of microvessel density by image analysis, to improve the accuracy and the objectivity of determination of the microvessel density. Methods: Four-micron-thick tissue sections of renal cell carcinoma samples were stained immunohistochemically for CD34. The regions with a high degree of vascularization were selected by an expert for digitization. Each image was digitized as a 24-bits/pixel image file with a resolution of 640×480 pixels. First, segmentation of the microvessels based on pixel classification using color features in hybrid color space was performed. After use of a correction process for microvessels with discontinuities and separation of touching microvessels, we counted the number of microvessels for the microvessel density measurement. Results: The result was evaluated by comparison with manual quantification of the same images. The comparison revealed that our computerized microvessel quantification was highly correlated with manual counting by a pathologist. Conclusion: The results indicate that our method is better than the conventional computerized image analysis methods.
Abstract
Objective: The most widely used method for quantifying new blood vessel growth in tumor angiogenesis is the determination of microvessel density, which is reported to be associated with tumor progression and metastasis, and a prognostic indicator of patient outcome. In this study, we propose a method for the determination of microvessel density by image analysis, to improve the accuracy and the objectivity of determination of the microvessel density. Methods: Four-micron-thick tissue sections of renal cell carcinoma samples were stained immunohistochemically for CD34. The regions with a high degree of vascularization were selected by an expert for digitization. Each image was digitized as a 24-bits/pixel image file with a resolution of 640×480 pixels. First, segmentation of the microvessels based on pixel classification using color features in hybrid color space was performed. After use of a correction process for microvessels with discontinuities and separation of touching microvessels, we counted the number of microvessels for the microvessel density measurement. Results: The result was evaluated by comparison with manual quantification of the same images. The comparison revealed that our computerized microvessel quantification was highly correlated with manual counting by a pathologist. Conclusion: The results indicate that our method is better than the conventional computerized image analysis methods.
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