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Updated: Apr 2, 2026

Using Computer Vision Libraries to Streamline Nuclei Quantification

Using Computer Vision Libraries to Streamline Nuclei Quantification

Published on: June 6, 2025

An Automatic Learning-Based Framework for Robust Nucleus Segmentation.

Fuyong Xing, Yuanpu Xie, Lin Yang

IEEE Transactions on Medical Imaging

|September 29, 2015

Summary

This summary is machine-generated.

This study introduces a novel deep learning framework for accurate nucleus segmentation in histopathology images, crucial for disease detection like brain tumors and breast cancer. The method ensures shape preservation and works across various staining techniques.

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Last Updated: Apr 2, 2026

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Exploiting Live Imaging to Track Nuclei During Myoblast Differentiation and Fusion

Exploiting Live Imaging to Track Nuclei During Myoblast Differentiation and Fusion

Published on: April 13, 2019

Area of Science:

Digital pathology
Computational imaging
Biomedical image analysis

Background:

Accurate nucleus segmentation is vital for quantitative analysis in histopathology, aiding disease detection.
Current methods face challenges due to the complexity and varied staining of histopathology images.

Purpose of the Study:

To develop a robust, learning-based framework for automatic nucleus segmentation with shape preservation.
To improve the accuracy and generalizability of nucleus segmentation across diverse histopathology datasets.

Main Methods:

A deep convolutional neural network (CNN) generates initial probability maps.
An iterative region merging approach refines shape initialization.
A novel segmentation algorithm combines sparse shape models and deformable models for individual nucleus separation.

Main Results:

The proposed framework demonstrates superior performance on large-scale, diverse histopathology datasets.
The method is effective across different tissue types and staining preparations.
Comparative experiments show improved results over state-of-the-art approaches.

Conclusions:

The developed framework offers a robust and generalizable solution for nucleus segmentation in digital pathology.
This approach supports automated quantitative analysis for improved disease characterization and early detection.