To illustrate how the presented tool is used to combine the different methods using the graphical user interface, four use cases are presented that represent common tasks in histopathology.
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The involved image analysis methods are stable for biological variations and variations in staining intensity and are fast enough to analyse WSIs successfully within reasonable time. The tool is built to provide a simple and intuitive interface and it is therefore easy enough to learn for a user to handle without prior knowledge of image analysis. It can be used for detection, classification and quantification of sub-cellular structures, such as nuclei, cytoplasm, membrane and gene probes in histological images. This paper presents a software tool that can be used for extracting and quantifying sub-cellular data from whole-slide images (WSIs) using computerised image analysis.
The problem with many of these methods is that they are not made accessible for the pathology community and when they are, the users often experience a trade-off between simplicity of use and flexibility for complex research applications.
#Manulelito c metaz manual#
The use of computer-assisted analysis of digital images of tissue samples can be an alternative to the more labour-intensive manual identification and classification, and numerous are the methods that have been developed to quantify protein expression in tissue images, offering both objectivity and reproducibility. Quantification of protein expression levels with sub-cellular resolution in histological images is important in many applications, such as biomarker discovery and cancer treatment.
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