Prostate cancer is currently a leading cause of deaths from malignancies in American and European men [11]. Owing improved methods of detection the amount of cases being reported has increased sharply. The clinical course of prostate cancer is often unpredictable, and various efforts have been undertaken to improve reliability of prognostic parameters. Morphological grading, staging, and tumor size are mainly used to assess the malignant potential of prostate cancer [7]. Besides these methods new techniques like nucleic acid fluorescence in situ hybridization (FISH) become increasingly important in diagnostic and research pathology. By FISH numerical chromosome aberrations e.g. gains or losses of specific single chromosomes (e.g. trisomy or monosomy) can be detected. Several studies have shown [3, 8] that trisomy of chromosome 7 is found in malignant and non-malignant tumors of lung, kidney, brain, as well as in tumors of the prostate. Considering the biological significance of this alteration being still under discussion Zitzelsberger et al. [18] have shown that trisomy of chromosome 7 may be used as prognostic factor because of correlation with the development of lymph node metastases and with grade of differentiation. Therefore, an improved image analysis procedure for quantitative evaluation of FISH in sections of prostate cancer has been established to simplify its quantitative evaluation, to confirm its prognostic value as well as to correlate FISH data with clinical follow-up in the long run.
The reliable count of FISH signals in tumor tissue necessitates a clear recognition of the tumor cells. This is only possible in sections with recognisable morphology. Standard histological sections contain only few cell nuclei completely inside the section. Only for those nuclei a reliable count of FISH signals can be assured [1], hence thick sections have to be analysed for sufficient number of cells.
However such thick sections cannot be analysed in 2-D because of the degraded image quality and of the possible and frequent occlusion of FISH signals. The latter can be suspected from the relatively stable amount of monosomies mostly independent of the number of tri- and polysomies [16].
In addition the analysis of 3-D images necessitates new accustoming for the pathologists. Since the actual state of 3-D data representation (cyberspace, virtual reality) is far away from routine applicability the interactive display and processing part has to be as near as possible to the typical observation behaviour of pathologists.
The seemingly simple task of signal counts becomes difficult by the condition that the count has to be done related to a single cell nucleus. As far as these nuclei are isolated it is easy to estimate the membership of a signal to a nucleus. However in most tumor tissues this relation is difficult or even sometimes impossible to determine. This is valid for visual inspection and even more for computer based analysis. It is a well known fact that segmentation is the most cumbersome task in every digital image analysis approach. Recently another graphical user interface for visual inspection was presented [2] using primarily volume rendering techniques for display. In our presented approach we tried to mimic the inspection behaviour of pathologists by mainly presenting sections, however volume rendering is possible too. The interactive inspection part is already used for a comparison of thin and thick section FISH count already reported in [1].
Concerning segmentation of nuclei in 3-D in [4, 17, 15] proposed segmentation methods mainly base on 2-D segmentation schemes and more or less elaborated merging operations. As mentioned above the segmentation of isolated nuclei is a minor problem. Therefore we try to tackle more the problem of connected nuclei by interactive means.