ABSTRACT
Computerized image analysis (IA) can provide quantitative and repeatable object measurements by means of methods such as segmentation, indexation, classification, etc. Embedded in reliable automated systems, IA could help pathologists in their daily work and thus contribute to more accurate determination of prognostic histological factors on whole slide images. One of the key concept pathologists want to dispose of now is a numerical estimation of heterogeneity. In this study, the objective is to propose a general framework based on the diffusion maps technique for measuring tissue heterogeneity in whole slide images and to apply this methodology on breast cancer histopathology digital images.
Subject(s)
Breast Neoplasms/classification , Breast Neoplasms/pathology , Cytodiagnosis/methods , Image Interpretation, Computer-Assisted/methods , Microscopy/methods , Pattern Recognition, Automated/methods , Algorithms , Female , Humans , Image Enhancement/methods , Reproducibility of Results , Sensitivity and SpecificityABSTRACT
BACKGROUND: Currently available microscope slide scanners produce whole slide images at various resolutions from histological sections. Nevertheless, acquisition area and so visualization of large tissue samples are limited by the standardized size of glass slides, used daily in pathology departments. The proposed solution has been developed to build composite virtual slides from images of large tumor fragments. MATERIALS AND METHODS: Images of HES or immunostained histological sections of carefully labeled fragments from a representative slice of breast carcinoma were acquired with a digital slide scanner at a magnification of 20×. The tiling program involves three steps: the straightening of tissue fragment images using polynomial interpolation method, and the building and assembling of strips of contiguous tissue sample whole slide images in × and y directions. The final image is saved in a pyramidal BigTiff file format. The program has been tested on several tumor slices. A correlation quality control has been done on five images artificially cut. RESULTS: Sixty tumor slices from twenty surgical specimens, cut into two to twenty six pieces, were reconstructed. A median of 98.71% is obtained by computing the correlation coefficients between native and reconstructed images for quality control. CONCLUSIONS: The proposed method is efficient and able to adapt itself to daily work conditions of classical pathology laboratories.
