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Analytical Cellular Pathology
Volume 15 (1997), Issue 1, Pages 19-29

Groping for Quantitative Digital 3-D Image Analysis: An Approach to Quantitative Fluorescence In Situ Hybridization in Thick Tissue Sections of Prostate Carcinoma

Karsten Rodenacker, Michaela Aubele, Peter Hutzler, and P. S. Umesh Adiga

GSF National Research Center for Environment and Health, Institute of Pathology, Ingolstädter Landstr. 1, D‐85764 Neuherberg, Germany

Received 10 February 1997; Revised 28 May 1997; Accepted 20 June 1997

Copyright © 1997 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In molecular pathology numerical chromosome aberrations have been found to be decisive for the prognosis of malignancy in tumours. The existence of such aberrations can be detected by interphase fluorescence in situ hybridization (FISH). The gain or loss of certain base sequences in the desoxyribonucleic acid (DNA) can be estimated by counting the number of FISH signals per cell nucleus. The quantitative evaluation of such events is a necessary condition for a prospective use in diagnostic pathology. To avoid occlusions of signals, the cell nucleus has to be analyzed in three dimensions. Confocal laser scanning microscopy is the means to obtain series of optical thin sections from fluorescence stained or marked material to fulfill the conditions mentioned above. A graphical user interface (GUI) to a software package for display, inspection, count and (semi‐)automatic analysis of 3‐D images for pathologists is outlined including the underlying methods of 3‐D image interaction and segmentation developed. The preparative methods are briefly described. Main emphasis is given to the methodical questions of computer‐aided analysis of large 3‐D image data sets for pathologists. Several automated analysis steps can be performed for segmentation and succeeding quantification. However tumour material is in contrast to isolated or cultured cells even for visual inspection, a difficult material. For the present a fully automated digital image analysis of 3‐D data is not in sight. A semi‐automatic segmentation method is thus presented here.