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Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

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Weakly supervised nuclei segmentation based on pseudo label correction and uncertainty denoising.

Xipeng Pan¹, Shilong Song¹, Zhenbing Liu¹

¹Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology, Guilin, 541004, Guangxi, China.

Artificial Intelligence in Medicine

|April 2, 2025

Summary

This study introduces a novel two-stage weakly supervised model for nuclei segmentation in histopathology images using only point annotations. The method achieves superior performance compared to existing point-label-based approaches.

Keywords:

Nuclei segmentation Point annotation Pseudo label denoising Weak supervision

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Area of Science:

Digital pathology
Medical image analysis
Computational biology

Background:

Accurate nuclei segmentation is crucial for computer-aided histopathology, but manual annotation is laborious.
Existing fully supervised methods require precise pixel-level annotations, which are time-consuming to obtain.

Purpose of the Study:

To develop a two-stage weakly supervised model for nuclei segmentation using only point annotations.
To improve the efficiency and accuracy of nuclei segmentation in whole slide images.

Main Methods:

A two-stage model employing coarse and fine segmentation phases.
Utilizes Voronoi diagrams and K-means clustering for initial supervision.
Incorporates an image adaptive clustering pseudo-label algorithm and Multi-scale Feature Fusion (MFF) module.
Employs Exponential Moving Average for cluster label Correction (EMAC) and uncertainty estimation for denoising.

Main Results:

The proposed method achieves superior performance on MoNuSeg and TNBC public benchmarks.
Demonstrates effectiveness in nuclei segmentation using only point annotations.
Outperforms existing nuclei segmentation methods that rely on point labels.

Conclusions:

The developed weakly supervised model offers an efficient and accurate solution for nuclei segmentation.
Point annotations are sufficient for training a high-performing segmentation model.
The method has significant implications for automated histopathology analysis.