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The ability of induced pluripotent stem cells or iPSCs to differentiate into most body cell types has stimulated repair and regenerative medicine research over the past few decades. iPSC-derived blood cells, hepatocytes, beta islet cells, cardiomyocytes, neurons, and other cell types can repair injuries or regenerate damaged tissue in diseases such as diabetes and neurodegenerative disorders.
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How does a complex organism such as a human develop from a single cell? It all starts from a single fertilized egg which gives rise to a vast array of cell types, such as nerve cells, muscle cells, and epithelial cells that characterize the adult? Throughout development and adulthood, cellular differentiation leads cells to assume their final morphology and physiology. Differentiation is the process by which unspecialized cells become specialized to carry out distinct functions.
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A cell line is a population of cells grown in vitro that can be subcultured over several generations. Normal cells cease to divide after a certain number of cell divisions, a process known as replicative senescence. This number, called the Hayflick limit, was conceptualized by Leonard Hayflick in 1961 when he observed that fetal cells grown in culture could only divide 40-60 times. This limit is due to the shortening of the telomeres during each round of cell division, preventing cell division...
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Related Experiment Video

Updated: Aug 20, 2025

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

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Scellseg: A style-aware deep learning tool for adaptive cell instance segmentation by contrastive fine-tuning.

Dejin Xun1, Deheng Chen2, Yitian Zhou3

  • 1Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.

Iscience
|November 25, 2022
PubMed
Summary

Scellseg is a new adaptive pipeline for cell segmentation that uses a style-aware model and contrastive fine-tuning. It achieves state-of-the-art results on diverse datasets with minimal manual effort, enabling biologists to create specialized models easily.

Keywords:
Artificial intelligenceBioinformaticsCell biology

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Area of Science:

  • Computational Biology
  • Bioimage Analysis
  • Machine Learning for Microscopy

Background:

  • Deep learning cell segmentation is vital for analyzing large biological datasets.
  • Limited annotated data hinders specialist algorithm development, while generalist models lack experiment-specific adaptation.

Purpose of the Study:

  • To introduce Scellseg, an adaptive pipeline for efficient and accurate cell segmentation.
  • To address the challenge of limited annotated data in deep learning-based cell segmentation.

Main Methods:

  • Utilizes a style-aware pre-trained model combined with contrastive fine-tuning on unlabeled data.
  • Employs an adaptive pipeline for self-improving segmentation models.
  • Develops a graphical user interface for user-friendly specialization.

Main Results:

  • Achieves state-of-the-art transferability in average precision and Aggregated Jaccard Index across diverse datasets.
  • Demonstrates that model performance plateaus after fine-tuning on approximately eight images.
  • Shows that Scellseg can create specialist models with minimal manual annotation effort.

Conclusions:

  • Scellseg offers a powerful and efficient solution for cell segmentation in biological research.
  • The adaptive pipeline and few-shot learning capability significantly reduce the need for extensive manual annotation.
  • The user-friendly interface democratizes the creation of customized cell segmentation models for biologists.