Automated and quantitative histological analysis can improve diagnostic efficacy in colon sections. Our objective was to develop a parameter set for automated classification of aspecific colitis, ulcerative colitis, and Crohn's disease using digital slides, tissue cytometric parameters, and virtual microscopy. Routinely processed hematoxylin-andeosin-stained histological sections from specimens that showed normal mucosa (24 cases), aspecific colitis (11 cases), ulcerative colitis (25 cases), and Crohn's disease (9 cases) diagnosed by conventional optical microscopy were scanned and digitized in high resolution (0.24 mm/pixel). Thirty-eight cytometric parameters based on morphometry were determined on cells, glands, and superficial epithelium. Fourteen tissue cytometric parameters based on ratios of tissue compartments were counted as well. Leave-one-out discriminant analysis was used for classification of the samples groups. Cellular morphometric features showed no significant differences in these benign colon alterations. However, gland related morphological differences (Gland Shape) for normal mucosa, ulcerative colitis, and aspecific colitis were found (P < 0.01). Eight of the 14 tissue cytometric related parameters showed significant differences (P < 0.01). The most discriminatory parameters were the ratio of cell number in glands and in the whole slide, biopsy/gland surface ratio. These differences resulted in 88% overall accuracy in the classification. Crohn's disease could be discriminated only in 56%. Automated virtual microscopy can be used to classify colon mucosa as normal, ulcerative colitis, and aspecific colitis with reasonable accuracy. Further developments of dedicated parameters are necessary to identify Crohn's disease on digital slides. ' 2008 International Society for Analytical Cytology

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Background: Automated virtual microscopy of specimens from gastrointestinal biopsies is based on cytometric parameters of digitized histological sections. To our knowledge, cytometric parameters of gastritis and of adenocarcinoma have yet to be fully characterized. Our objective was to classify gastritis and adenocarcinoma based on cytometric parameters. We hypothesized that automated virtual microscopy using this novel classification can reliably diagnose gastritis and adenocarcinoma.Methods: Routinely processed hematoxylin-and-eosin-stained histological sections from specimens that showed normal mucosa (14 cases), gastritis (35 cases), and adenocarcinoma (30 cases) diagnosed by conventional optical microscopy were scanned and digitized at high resolution. Thirty-eight cytometric parameters based on density and morphometry were applied to glands and superficial epithelium. Twelve cytometric parameters based on cytologic detail were applied to individual cells.Results: Statistically significant differences in cytometric parameters for normal mucosa, gastritis, and adenocarcinoma were found. The most discriminatory parameter was the ratio of the total number of cells to the number of interstitial cells. These differences correctly classified adenocarcinoma at 100% accuracy and overall correctness was 86%.Conclusions: We describe a novel method of analyzing gastric mucosal histology based on cytometric parameters. Automated virtual microscopy can be used to classify gastric mucosa as normal, gastritis, or adenocarcinoma with reasonable accuracy. Further research is necessary to determine whether automated virtual microscopy can subclassify gastric mucosal histology in greater detail. q 2006 International

OBJECTIVE: The application ofan electronic slide and a software simulated virtual microscope can contribute to a more efficient, convenient histological analysis. These techniques would also support the automation of histological analysis and three dimensional reconstruction of histological objects. STUDY DESIGN: A fully computer controlled microscope (Axioplan 2 MOT), video camera(Grundig FAC 830) and an Intel Pentium II based PC were used for the development ofthe electronic slide. The applied frame grabber had 640x560 resolution, 64 kb colour depth. A program was developed. called Pyramid, for the scanning of an entire slide.Autofocusing, image reduction and frame joining algorithms were implemented in the virtual microscope application. RESULTS :The autofocusing and digitisation of one image segment (400x magnification, 0,0725 mm2) took 8 seconds.The harddisk volume of one frame is between 60 and 100 kilobytes (kb) after JPEG compression. The overall harddisk place for a gastric biopsy is around 130-1 50 megabytes. The Pyramid program contains routines for electronic evaluation of the slide. The major microscopic functions are implemented : moving in any free directions in discrete or continuos steps, magnifications on a discrete scale (400,200, lOOx), and in continuos scale. Up to lo notes can be placed on any place ofthe slide and can be retrieved within a second. The program can be used in local area networks for slide evaluations. CONCLUSIONS : The scanning speed is now too low for routine application, however with further developments in data storage and imaging technology, the electronic slide and the virtual microscope can be alternative techniques in the computerisation of the histology laboratory. After the scanning of consecutive sections and a mathematical matching procedure supracellular organisations from gastric biopsies were reconstructed giving new insight into tumour growth.

Conventional optical microscopy of specimens from colorectal biopsies commonly produces diagnostic errors due to incomplete sampling or poor orientation. Obtaining additional sections or re-embedding may help avoid these errors, but can prolong turnaround time. We describe new technology, which incorporates exhaustive sectioning, 3-dimensional reconstruction, and virtual microscopy, that may eliminate these problems by enabling pathologists to rapidly examine entire specimens and convert poorly oriented mucosa to well-oriented mucosa.