TY - GEN
T1 - Three-dimensional quantitative analysis of cell nuclei for grading renal cell carcinoma
AU - Choi, H. J.
AU - Kim, T. Y.
AU - Cho, N. H.
AU - Jeong, G. B.
AU - Huh, Y.
AU - Choi, H. K.
PY - 2005
Y1 - 2005
N2 - In this paper, we have proposed a method for renal cell carcinoma (RCC) grading, using a three-dimensional (3D) quantitative analysis of cell nuclei based on digital image cytometry. We acquired volumetric RCC data for each grade using confocal laser scanning microscopy (CLSM) and developed a method for grading RCC using 3D visualization and quantitative analysis of cell nuclei. First, we used a method of segmenting cell nuclei based on Pun's method. Second, to determine quantitative features, we used a 3D labeling method based on slice information. After applying the labeling algorithm, we determined the measurements of cell nuclei using 3D quantitative analysis. To evaluate which of the quantitative features provided by 3D analysis could contribute to diagnostic information and could increase accuracy in nuclear grading, we analyzed statistical differences in 3D features among the grades. We compared features measured in two dimensions (diameter, area, perimeter, and circularity) with features measured in three dimensions (volume, surface area, and spherical shape factor) between identical cell nuclei by using regression analysis. For 3D visualization, we used a contour-based method for surface rendering. We found a statistically significant correlation between the nuclear grade and the 3D morphological features. Comparing our results to an ideal RCC grading system, we found that our nuclear grading system based on the 3D features of a cell nucleus provides distinct dividing points between grades and also provides data that can be easily interpreted for diagnoses. 3D visualization of cell nuclei offers a realistic display and additional valuable medical information that can lead to an objective diagnosis. This method could overcome the limitations inherent in 2D analysis and could improve the accuracy and reproducibility of quantification of cell nuclei. Our study showed that a nuclear grading system based on the 3D features of a cell nucleus might be an ideal grading system.
AB - In this paper, we have proposed a method for renal cell carcinoma (RCC) grading, using a three-dimensional (3D) quantitative analysis of cell nuclei based on digital image cytometry. We acquired volumetric RCC data for each grade using confocal laser scanning microscopy (CLSM) and developed a method for grading RCC using 3D visualization and quantitative analysis of cell nuclei. First, we used a method of segmenting cell nuclei based on Pun's method. Second, to determine quantitative features, we used a 3D labeling method based on slice information. After applying the labeling algorithm, we determined the measurements of cell nuclei using 3D quantitative analysis. To evaluate which of the quantitative features provided by 3D analysis could contribute to diagnostic information and could increase accuracy in nuclear grading, we analyzed statistical differences in 3D features among the grades. We compared features measured in two dimensions (diameter, area, perimeter, and circularity) with features measured in three dimensions (volume, surface area, and spherical shape factor) between identical cell nuclei by using regression analysis. For 3D visualization, we used a contour-based method for surface rendering. We found a statistically significant correlation between the nuclear grade and the 3D morphological features. Comparing our results to an ideal RCC grading system, we found that our nuclear grading system based on the 3D features of a cell nucleus provides distinct dividing points between grades and also provides data that can be easily interpreted for diagnoses. 3D visualization of cell nuclei offers a realistic display and additional valuable medical information that can lead to an objective diagnosis. This method could overcome the limitations inherent in 2D analysis and could improve the accuracy and reproducibility of quantification of cell nuclei. Our study showed that a nuclear grading system based on the 3D features of a cell nucleus might be an ideal grading system.
KW - 3D feature extraction
KW - 3D labeling
KW - 3D visualization
KW - Confocal laser scanning microscopy
KW - Histopathological grading system
KW - Renal cell carcinoma tissue section image
KW - Segmentation
UR - http://www.scopus.com/inward/record.url?scp=33745226673&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33745226673&partnerID=8YFLogxK
U2 - 10.1109/HEALTH.2005.1500433
DO - 10.1109/HEALTH.2005.1500433
M3 - Conference contribution
AN - SCOPUS:33745226673
SN - 0780389409
SN - 9780780389403
T3 - Proceedings of the 7th International Workshop on Enterprise Networking and Computing in Healthcare Industry, HEALTHCOM 2005
SP - 179
EP - 186
BT - Proceedings of 7th International Workshop on Enterprise Networking and Computing in Healthcare Industry, HEALTHCOM 2005
T2 - 7th International Workshop on Enterprise Networking and Computing in Healthcare Industry, HEALTHCOM 2005
Y2 - 23 June 2005 through 25 June 2005
ER -