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Flow Cytometry

Flow Cytometry

The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

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Deep learning for computational cytology: A survey.

Hao Jiang¹, Yanning Zhou², Yi Lin¹

¹Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.

Medical Image Analysis

|December 1, 2022

Summary

Deep learning (DL) advances computational cytology for cancer screening by analyzing digitized images. This survey covers DL methods, datasets, applications like cell classification and segmentation, and future research directions.

Keywords:

Artificial intelligence Cancer screening Computational cytology Deep learning Pathology Survey

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Area of Science:

Medical image computing
Computational pathology
Artificial intelligence in oncology

Background:

Computational cytology analyzes digitized cytology images for cancer screening.
Deep learning (DL) has shown significant achievements in medical image analysis.
There is a growing body of research on DL applications in cytological studies.

Purpose of the Study:

To survey over 120 publications on DL-based cytology image analysis.
To investigate advanced DL methods and comprehensive applications in computational cytology.
To discuss current challenges and future research directions in the field.

Main Methods:

Introduction to various deep learning schemes: fully supervised, weakly supervised, unsupervised, and transfer learning.
Systematic summarization of public datasets and evaluation metrics used in DL for cytology.
Categorization of versatile cytology image analysis applications.

Main Results:

DL methods are increasingly applied to diverse cytology image analysis tasks.
Key applications include cell classification, slide-level cancer screening, and nuclei/cell detection and segmentation.
A comprehensive overview of existing DL approaches, datasets, and evaluation strategies is presented.

Conclusions:

Deep learning is a powerful tool revolutionizing computational cytology and cancer screening.
Further research is needed to address current challenges and explore new potential directions.
The field requires continued investigation into advanced DL techniques and their clinical translation.