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Cell separation was first achieved in 1964 by S. H. Seal, who separated large tumor cells from the smaller blood cells using filtration. Two years later, Pohl and Hawk performed experiments on how cells respond differently to a nonuniform electric field based on the cell type. Such observations were the inception of cell separation methods, which allow isolating a single cell type from a heterogeneous sample.

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From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

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SAMCell: Generalized Label-Free Biological Cell Segmentation with Segment Anything.

Alexandra D VandeLoo¹, Nathan J Malta², Emilio Aponte²

¹School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA.

Biorxiv : the Preprint Server for Biology

|February 20, 2025

Summary

SAMCell automates cell segmentation in microscopy images, improving cell health analysis. This tool, based on the Segment Anything Model (SAM), requires no machine learning expertise for biologists.

Keywords:

Cell Segmentation Machine Learning Segment Anything Model

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Area of Science:

Cell Biology
Microscopy
Bioimage Analysis

Background:

Assessing cell morphology, confluency, and growth is crucial for cell health analysis in microscopy.
Manual analysis is laborious for high-throughput applications, necessitating automated solutions.
Existing automated cell segmentation methods often require expertise and annotated datasets.

Purpose of the Study:

To develop an automated cell segmentation tool for microscopy images.
To reduce the technical expertise and labor required for cell culturing analysis.
To improve the quality and efficiency of cell segmentation in biological research.

Main Methods:

A modified version of Meta's Segment Anything Model (SAM) was developed, named SAMCell.
SAMCell was trained on a large-scale dataset of diverse microscopy images.
A user-friendly graphical user interface (GUI) was created to facilitate tool usage.

Main Results:

SAMCell effectively performs cell segmentation across various cell types and confluency levels.
The model demonstrates robustness, working on cell types not included in training and images from different microscopes.
The GUI significantly lowers the technical barrier for utilizing automated microscopy analysis.

Conclusions:

SAMCell provides high-quality, automated cell segmentation results, surpassing previous methods.
The tool streamlines cell culturing workflows by reducing manual labor.
Biologists can now perform advanced image analysis without specialized machine learning knowledge.