Abstract
We present a novel method to assess the variations in protein expression and spatial heterogeneity of tumor biopsies with application in computational pathology. This was done using different antigen stains for each tissue section and proceeding with a complex image registration followed by a final step of color segmentation to detect the exact location of the proteins of interest. For proper assessment, the registration needs to be highly accurate for the careful study of the antigen patterns. However, accurate registration of histopathological images comes with three main problems: the high amount of artifacts due to the complex biopsy preparation, the size of the images, and the complexity of the local morphology. Our method manages to achieve an accurate registration of the tissue cuts and segmentation of the positive antigen areas.
Highlights
Cancer diagnosis is determined by broad image and pathology studies
We have presented a method to create tumor heterogeneity maps using a novel registration technique combined with a novel segmentation technique, both of which are completely automatic
The generation of the heterogeneity maps may fail if the sections are poorly stained and show no signal or if the slides have suffered extensive damage during sample preparation
Summary
Cancer diagnosis is determined by broad image and pathology studies. In these pathology studies, insight into molecular and cellular interactions, growth, and organization is obtained through careful analysis and observation of stained histological sections. Insight into molecular and cellular interactions, growth, and organization is obtained through careful analysis and observation of stained histological sections These studies are complex, long, subjective, and prone to error. Misdiagnosis could have fatal consequences, as would taking too long to determine the appropriate diagnosis. This process needs to be automatized, to make it fast, accurate, robust, and objective.
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