Seg_Cell: a newly developed Python tool for a fast and automatic counting of cells in a .lif file

The analysis of optical microscopy images to extract statistical information on tumoral cell morphology is crucial to a lot of biological research projects. However, currently available tools often lack the capability to iteratively analyse large datasets of images contained in a .lif file with high accuracy, while maintaining relatively low processing times per image. To remove this bottleneck, we developed a Python script that overcomes these limitations, enabling faster and more precise analysis of large image datasets containing hundred of cells per image. This new tool has proven to be highly accurate in counting the number of cells, while maintaining a low processing time per image.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-05-07
DOI
https://doi.org/10.5281/zenodo.15355994
Primary Topic
Cell Image Analysis Techniques
Type
article
Field-Weighted Citation Impact
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article

Seg_Cell: a newly developed Python tool for a fast and automatic counting of cells in a .lif file

Zenodo (CERN European Organization for Nuclear Research)
Cell Image Analysis Techniques
article

Seg_Cell: a newly developed Python tool for a fast and automatic counting of cells in a .lif file

article en

Abstract

The analysis of optical microscopy images to extract statistical information on tumoral cell morphology is crucial to a lot of biological research projects. However, currently available tools often lack the capability to iteratively analyse large datasets of images contained in a .lif file with high accuracy, while maintaining relatively low processing times per image. To remove this bottleneck, we developed a Python script that overcomes these limitations, enabling faster and more precise analysis of large image datasets containing hundred of cells per image. This new tool has proven to be highly accurate in counting the number of cells, while maintaining a low processing time per image.

Zenodo (CERN European Organization for Nuclear Research)
Italian Institute of Technology (IT)
Partnerships for the goals
Openalex Percentile: Top 64%
Cell Image Analysis Techniques
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