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Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

Cell-APP: A generalizable method for cell annotation and cell-segmentation model training.

Anish Virdi¹, Ajit P Joglekar^1,2

¹Department of Biophysics, University of Michigan, Ann Arbor, MI 48109, USA.

Biorxiv : the Preprint Server for Biology

|February 3, 2025

Summary

This summary is machine-generated.

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We developed a novel method to generate large training datasets for cell instance segmentation in microscopy images. This approach enables accurate classification of cells into dividing (m-phase) or non-dividing (interphase) states, improving systems biology research.

Area of Science:

Cellular biology
Computer vision
Microscopy imaging

Background:

High-throughput fluorescence microscopy is crucial for systems biology.
Accurate cell localization and quantification are essential for analyzing cellular processes.
Supervised deep learning excels at cellular instance segmentation but requires extensive annotated data.

Purpose of the Study:

To develop a generalizable method for generating large instance segmentation training datasets for tissue-culture cells.
To train vision transformer-based Mask R-CNN models for accurate cell segmentation and classification.
To address class imbalance issues in biological datasets for improved model performance.

Main Methods:

A novel method for generating synthetic instance segmentation training data for transmitted light microscopy images.

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Training of vision transformer (ViT) based Mask R-CNN models using generated datasets.

Implementation of probabilistically weighted loss functions and partisan data collection to handle class imbalance between m-phase and interphase cells.

Main Results:

Generation of large, high-quality instance segmentation training datasets for tissue-culture cells.
Development of highly accurate object detectors capable of segmenting and classifying diverse cell types.
Successful mitigation of dataset class imbalance, leading to improved model performance.

Conclusions:

The proposed method provides a generalizable approach for creating large-scale training data for cell instance segmentation.
The trained models demonstrate high accuracy in segmenting and classifying cells as m-phase or interphase.
The methodology is adaptable to various adherent tissue culture cell lines, offering broad applicability in biological research.