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Updated: Nov 2, 2025

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

Artificial Intelligence and Cellular Segmentation in Tissue Microscopy Images.

Madeleine S Durkee¹, Rebecca Abraham², Marcus R Clark²

¹Department of Radiology and the Committee on Medical Physics, University of Chicago, Chicago, Illinois.

The American Journal of Pathology

|June 15, 2021

Summary

This summary is machine-generated.

Artificial intelligence (AI) and deep learning enhance biomedical image analysis for high-throughput cell segmentation and data mining. These advanced methods improve accuracy, offering insights into cellular organization in various pathologies.

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Area of Science:

Biomedical Imaging
Computational Pathology
Immunology

Background:

Artificial intelligence (AI) and deep learning are increasingly used in biomedical imaging for tasks like object detection and image classification.
The development of novel deep neural network architectures and open-source software has improved the accuracy of cell detection and segmentation algorithms.
Automated cell segmentation enables the extraction of quantifiable cellular and spatio-cellular features from microscopy images.

Purpose of the Study:

To provide an overview of current state-of-the-art AI and deep learning methods for cell segmentation in microscopy images.
To highlight the application of these methods in data mining for biomedical research.
To discuss the insights gained into cellular organization in pathologies through automated analysis.

Main Methods:

Review of deep learning and AI-based techniques for image segmentation.
Analysis of computer-visualization methods applied to biomedical images.
Exploration of algorithms for cell detection and segmentation.

Main Results:

AI and deep learning significantly improve the accuracy of cell segmentation algorithms.
Automated segmentation allows for high-throughput mining of cellular and spatio-cellular features.
These techniques facilitate a deeper understanding of cellular organization in various pathologies.

Conclusions:

Deep learning and AI are transforming the analysis of microscopy images in biomedical disciplines.
Automated cell segmentation provides powerful tools for data mining and pathological insights.
The continued development of these technologies promises further advancements in high-throughput biomedical image analysis.