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A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

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Self-Refining Segment Anything Model for Nuclei Segmentation as Contrastive Learning Approach to Label-Efficient

Siwoo Nam¹, Sang Hyun Park²

¹Department of Robotics and Mechatronics Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Republic of Korea.

Diagnostics (Basel, Switzerland)

|May 13, 2026

Summary

This summary is machine-generated.

This study introduces a self-evolving framework for nuclei instance segmentation using sparse point annotations, enhancing digital pathology accuracy. The method refines learning targets adaptively, achieving state-of-the-art results for automated diagnostics.

Keywords:

Segment Anything Model (SAM)contrastive learning nuclei instance segmentation pseudo-labeling weakly supervised learning

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Area of Science:

Digital Pathology
Computational Biology
Medical Image Analysis

Background:

Precise nuclei instance segmentation is crucial for digital pathology.
Scarcity of pixel-level annotations limits deep learning model performance.

Purpose of the Study:

Develop a robust nuclei segmentation framework using sparse point annotations.
Extend the Segment Anything Model (SAM) for histopathology applications.
Improve automated diagnostic workflows in clinical settings.

Main Methods:

Propose a self-evolving framework utilizing sparse point annotations.
Implement a self-evolving labeling strategy with Exponential Moving Average (EMA) for adaptive target refinement.
Integrate instance-aware contrastive learning and consensus-based filtering.

Main Results:

Achieve state-of-the-art performance on CPM17, MoNuSeg, and CoNSeP datasets.
Demonstrate high accuracy across various backbones (ViT-B, ViT-H).
Validate the framework's effectiveness for nuclei instance segmentation.

Conclusions:

The self-refining approach transitions foundation models to specialized histopathology tools.
Offers an efficient and accurate solution for automated diagnostic workflows.
Enables reliable digital pathology through precise nuclei segmentation.