Digital Transformation in Pathology / What to consider when choosing an image analysis solution for phenotyping
Digital Transformation in Pathology podcast by Dr Aleksandra Zuraw from Digital Pathology Place – sponsored episode by Visiopharm.
In the final episode of our three part series about phenotyping, Dr Aleksandra Zuraw and our Scientific Consulting Sales Manager, Regan Baird, discuss considerations when choosing image analysis software for phenotyping.
The two main points to consider when choosing a phenotyping image analysis software are segmentation assistance and data visualization.
Before different markers are attributed to different cells in the tissue and cell phenotypes are determined, cell boundaries need to be delineated. The automatic delineation of these boundaries by image analysis software is called cell segmentation.
Cells in tissue slides can have different shapes and sizes, which depend on the plane of sectioning, heterogeneity of the investigated tissue, and the disease stage. This makes the task of segmentation challenging. Unlike in single-cell confocal microscopy images, where the cell borders are very well-demarcated, in tissue they often need to be estimated. A separate segmentation (e.g., membrane) marker can help significantly, but a perfect cell segmentation is not attainable.
To best estimate the cell boundaries, rule-based classical computer vision approaches or artificial intelligence (AI) – powered approaches can be used. In rule-based approaches, we are working with well-defined features on which the segmentation is based, but we need to make concessions. The AI-powered models are only as good as the example we train the models on. To combine the advantages of both, Visiopharm offers an AI-based nuclear segmentation as the starting point and a rule-based and marker-based second step to obtain the most reliable cell segmentation for phenotyping.
The adequate visualization and handling of the obtained data depend on the software used. To understand and interpret the multidimensional multiplex and phenotyping data we need to interpret graphs, plots, two-dimensional reduction plots, and other data visualizations for all the images in multiplex studies. In order to evaluate how well the phenotyping has performed and to export meaningful results, the correct visualization tools need to be used.
Runtime: 13 minutes
- How to make sense of multiplex data with phenotyping (part two)
- Introduction to multiplex for tissue image analysis (part one)
- AI-powered phenotyping of multiplexed images
- Phenotyping the tumor microenvironment on-demand webinar
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