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Interphase

Interphase

The cell cycle occurs over approximately 24 hours (in a typical human cell) and in two distinct stages: interphase, which includes three phases of the cell cycle (G1, S, and G2), and mitosis (M). During interphase, which takes up about 95 percent of the duration of the eukaryotic cell cycle, cells grow and replicate their DNA in preparation for mitosis.
Phases of Interphase
Following each period of mitosis and cytokinesis, eukaryotic cells enter interphase, during which they grow and replicate...

Interphase

Interphase

The cell cycle occurs over approximately 24 hours (in a typical human cell) and in two distinct stages: interphase, which includes three phases of the cell cycle (G1, S, and G2), and mitosis (M). During interphase, which takes up about 95 percent of the duration of the eukaryotic cell cycle, cells grow and replicate their DNA in preparation for mitosis.

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Related Experiment Video

Updated: Apr 5, 2026

Isolation and Staining of Mouse Skin Keratinocytes for Cell Cycle Specific Analysis of Cellular Protein Expression by Mass Cytometry

Isolation and Staining of Mouse Skin Keratinocytes for Cell Cycle Specific Analysis of Cellular Protein Expression by Mass Cytometry

Published on: May 9, 2019

Benchmarking human epithelial type 2 interphase cells classification methods on a very large dataset.

Peter Hobson¹, Brian C Lovell², Gennaro Percannella³

¹Sullivan Nicolaides Pathology, 134 Whitmore street, Taringa, Queensland 4068, Australia.

Artificial Intelligence in Medicine

|August 26, 2015

Summary

This summary is machine-generated.

Benchmarking computer-aided diagnosis systems for human epithelial type 2 (HEp-2) cell image classification shows that combining top methods improves accuracy. Highest performance uses kernelized support vector machines with local statistical features, but low contrast images remain challenging.

Keywords:

Computer-aided diagnosis systems Hep-2 cell classification Indirect immunofluorescence Large-scale benchmarking

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Analysis of Cell Cycle Position in Mammalian Cells

Analysis of Cell Cycle Position in Mammalian Cells

Published on: January 21, 2012

Area of Science:

Medical Imaging
Computer-Aided Diagnosis
Immunofluorescence Microscopy

Background:

Indirect immunofluorescence on HEp-2 cells is the gold standard for diagnosing connective tissue diseases.
This method is subjective, time-consuming, and labor-intensive, necessitating automated solutions.
Computer-aided diagnosis (CAD) systems aim to automate HEp-2 cell image classification.

Purpose of the Study:

To benchmark the performance of various HEp-2 interphase cell image classification methods.
To evaluate these methods on a very large dataset of over 68,000 images.
To assess the impact of combining top-performing methods.

Main Methods:

An international competition involving fourteen teams was held in conjunction with the International Conference of Image Processing (ICIP) 2013.
Each team's system was trained and tested on a large dataset of HEp-2 cell images with varying staining patterns and fluorescence intensities.
Methods were analyzed based on design choices and benchmarking results.

Main Results:

Staining pattern recognition accuracy ranged from 47.91% to 83.65%.
The performance difference between the top and seventh-ranked methods was minimal (5%).
Fusing the top seven methods achieved a recognition rate of 85.60%.

Conclusions:

Optimal performance is achieved using strong classifiers like kernelized support vector machines combined with local statistical features.
Certain staining patterns are inherently more difficult to classify accurately.
Image contrast and fluorescence intensity significantly impact classification performance, with low-contrast images yielding lower accuracy.