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Related Experiment Video

Updated: Oct 16, 2025

Using Computer Vision Libraries to Streamline Nuclei Quantification

Using Computer Vision Libraries to Streamline Nuclei Quantification

Published on: June 6, 2025

Local Integral Regression Network for Cell Nuclei Detection.

Xiao Zhou¹, Miao Gu¹, Zhen Cheng¹

¹Department of Automation, Tsinghua University, Beijing 100084, China.

Entropy (Basel, Switzerland)

|October 23, 2021

Summary

This summary is machine-generated.

We developed a new Local Integral Regression Network (LIRNet) for nuclei detection in histopathology images. This method significantly reduces annotation effort for both fully and weakly supervised learning frameworks.

Keywords:

convolutional neural networks fully supervised learning local integral regression nuclei detection weakly supervised learning

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Area of Science:

Digital pathology
Computational imaging
Biomedical image analysis

Background:

Nuclei detection is crucial in histopathology image analysis but challenged by cellular heterogeneity.
Current methods like segmentation and counting-based approaches require extensive manual annotations for training.
This annotation burden limits the scalability and efficiency of nuclei detection models.

Purpose of the Study:

To introduce a novel Local Integral Regression Network (LIRNet) for nuclei detection.
To enable both fully supervised learning (FSL) and weakly supervised learning (WSL) frameworks for nuclei detection.
To reduce the significant annotation effort and cost associated with training nuclei detection models.

Main Methods:

Proposed a Local Integral Regression Network (LIRNet) for nuclei detection.
Developed LIRNet to support both FSL and WSL frameworks.
LIRNet generates a density map for nuclei localization, minimizing post-processing dependency.

Main Results:

The FSL version of LIRNet achieved state-of-the-art performance in nuclei detection.
The WSL version demonstrated competitive detection accuracy with substantially reduced annotation effort (17.5% of full annotation).
LIRNet's density map output ensures robust nucleus localization independent of post-processing.

Conclusions:

LIRNet offers an effective solution for nuclei detection in histopathology.
The proposed network significantly lowers annotation costs through its WSL capabilities.
LIRNet advances the field by providing accurate and efficient nuclei detection with reduced expert labeling.