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Related Experiment Video

Updated: Nov 6, 2025

Quantitative Analysis of Protein Expression to Study Lineage Specification in Mouse Preimplantation Embryos

Quantitative Analysis of Protein Expression to Study Lineage Specification in Mouse Preimplantation Embryos

Published on: February 22, 2016

Automatic wavelet-based 3D nuclei segmentation and analysis for multicellular embryo quantification.

Tzu-Ching Wu¹, Xu Wang^1,2,3, Linlin Li²

¹Department of Agriculture and Biological Engineering, Purdue University, West Lafayette, IN, 47907, USA.

Scientific Reports

|May 11, 2021

Summary

This summary is machine-generated.

We developed a novel wavelet-based method for robust cell segmentation in biological images. This size-dependent approach accurately identifies individual cells in complex tissues and embryos without preprocessing.

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Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

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Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Published on: April 30, 2019

Area of Science:

Developmental Biology
Image Analysis
Cell Biology

Background:

Accurate cell identification is crucial for quantitative image analysis in biological research.
Existing segmentation methods often require preprocessing and are sensitive to image noise.

Purpose of the Study:

To develop a robust and efficient cell segmentation method.
To enable precise quantitative analysis of cellular patterns in developing organisms.

Main Methods:

A size-dependent wavelet-based segmentation algorithm was developed.
The method operates without preprocessing, filtering, or fine-tuning.
Applied to zebrafish embryonic development for nuclei segmentation and analysis.

Main Results:

The wavelet-based method demonstrated robust segmentation performance.
Achieved high True Positive rate, Precision, and segmentation accuracy.
Outperformed commonly used segmentation techniques.

Conclusions:

The developed segmentation approach offers a reliable tool for single-cell precision analysis.
It is highly tolerant to non-uniform and noisy image datasets.
Facilitates quantitative developmental and tissue patterning studies.