![stereology determining sampling stereology determining sampling](https://www.frontiersin.org/files/Articles/380792/fnana-12-00073-HTML/image_m/fnana-12-00073-g010.jpg)
In order to reduce the expertise workload, a stereological test grid for point counting is over imposed onto VS in ImageScope viewer (Aperio Technologies, Inc). The tools needed for this study were developed in Python language ( ) with the help of specialized modules (PIL: Python Imaging Library and SciPy: ). Their mean size is about 65000x43000 pixels 2 and each holds about 350 MB on a hard disk. Images have been acquired at X20 (0.5 µm per pixel), using a digital slide scanner (ScanScope CS from Aperio Technologies, Inc) and then stored in TIFF 6.0 image file format with a 30% jpeg compression. Its ability to be embedded into a computer-aided diagnosis system (CADS) is illustrated by building an unbiased image database containing representative patches of a benign tumor (Fibroadenoma) and by testing the discrimination between a benign tumor and a malignant tumor (Fibroadenoma vs Comedo carcinoma). The main goal here is to collect a useful number of image patches corresponding to a given histological type. Images used for illustrating the strategy are VS of histological sections of breast tumors, stained in the same laboratory according to the Hematoxylin-Eosin-Safron protocol and acquired with the same digital scanner. The practical application illustrating this framework makes use of VS of breast tumors. The original strategy exposed in this paper consists in starting from a collection of VS, then taking advantage of stereological sampling methods and diffusion maps, to finally compute a knowledge image database containing a small number of image patches that are representative of a given histological type or subtype. This work relates to the medical image processing and retrieval field, with the goal to develop and propose a functional computer-aided diagnosis system based on a knowledge database. Among these reduction methods, the diffusion maps provide a very attractive framework for processing and visualizing huge non-linear bulk data. A data reduction has then to be conducted in order to keep a proper right number of representative elements. However, even if the working area is smaller, the number of selected regions can be very high and can include many redundant elements. Systematic sampling resulting from a random starting point with a fixed periodic interval is able to reduce the area to be analyzed, while preserving the collection of varied and characteristic regions encountered in a virtual slide (VS) of a tumor. The sampling tools offered by stereology can be of great help in this context. It is then mandatory to find wiser solutions leading to an unbiased collection of image databases.
![stereology determining sampling stereology determining sampling](https://www.researchgate.net/profile/Jacob-Jelsing/publication/236600849/figure/fig1/AS:299433670529024@1448401958983/Tissue-sampling-and-stereological-probes-The-intestinal-tract-was-divided-into-the.png)
A bias is then introduced in the process as this choice is obviously subjective. But, as it is almost impossible for a pathologist to manually segment such a large image, and a fortiori many of them (the estimated time being hundred hours), the current practice consists in manually selecting some 'representative areas'. Because of tumor heterogeneity, it is essential to build image knowledge databases containing representative features of the various morphological types of lesions before considering implementing computer-aided diagnosis systems. More and more introduced in pathology departments, these systems however generate very large images which frequently exceed several Gigabytes. The recent marketing of digitizers now allows visualizing the entire histological slide at high resolution, while limiting time expense and artifacts previously encountered with image tiling methods.
![stereology determining sampling stereology determining sampling](https://www.stereology.info/wp-content/uploads/2013/10/Volume_pic2.png)
![stereology determining sampling stereology determining sampling](https://europepmc.org/articles/PMC4380666/bin/nihms672650f1.jpg)
Indeed, it may help pathologists in their daily practice in finding objective criteria for differential diagnosis or quantifying prognostic markers. Fully automated image processing is able to provide a solution to this problem. While pathologist population tends to dramatically dropped, the number of pathological cases to examine increases steadily (mainly due to the new screening campaigns).