Image analysis with Tissue Recognition for ROA

The Tissue Recognition for ROA program enables the creation of a region of analysis based on the characteristics of the tissues found in an image. This algorithm enables the quantifiable region to be isolated from the rest of the tissue by identifying tumors, stromata, necrosis, healthy tissue,etc. It also enables identification of typical architectures on IHC or HE slides so they can be measured or quantified during a second phase.

The protocol :

The stages of image analysis with the Tissue Recognition for ROA algorithm are as follows :

  • Creation of a learning model
    The RFT tool enables a learning base to be created that enables cells presenting certain characteristics to be identified. The user then stains the different objects using a brush so that the algorithm can automatically determine the classificatory characteristics.
  • Selection of categories for analysis
    The results of the learning algorithm-based image categorization are shown. This enables the user to select the categories they wish to analyze. As such, only the area or areas selected will be kept.
  • Mask post-processing
    The results of the image processing are displayed in accordance with the selected categories. There are tools for carrying out post-processing of the analysis at this stage: adjustment of object size, contour smoothing, etc.

The mask created by the algorithm is displayed on the region of analysis (ROA). This latter can then be processed by an image analysis and quantification program using our CaloPix software.

Discover how to create an RFT project