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Updated: Jun 28, 2026

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

Fine-grained multiclass nuclei segmentation with molecular empowered all-in-SAM model.

Xueyuan Li¹, Can Cui², Ruining Deng²

¹Vanderbilt University, Data Science Institute, Nashville, Tennessee, United States.

Journal of Medical Imaging (Bellingham, Wash.)

|September 8, 2025

Summary

The molecular empowered all-in-SAM model enhances computational pathology by improving nuclei segmentation and cell classification. This approach reduces annotation workload and increases accessibility for precise biomedical image analysis.

Keywords:

cell segmentation deep learning foundation model image annotation molecular empowered learning

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Last Updated: Jun 28, 2026

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Using Computer Vision Libraries to Streamline Nuclei Quantification

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Published on: June 6, 2025

Area of Science:

Computational pathology
Biomedical image analysis
Artificial intelligence in medicine

Background:

Vision foundation models (VFMs), like Segment Anything Model (SAM), are advancing computational pathology.
Current VFMs struggle with fine-grained semantic segmentation for specific cell types.
Nuclei segmentation is crucial for accurate pathology image analysis.

Purpose of the Study:

To introduce the molecular empowered all-in-SAM model for enhanced computational pathology.
To leverage VFMs for improved nuclei and cell segmentation.
To address the limitations of general VFMs in fine-grained semantic segmentation.

Main Methods:

Developed a full-stack approach integrating molecular empowered learning, SAM adapter, and molecular oriented corrective learning.
Enabled annotation-engaging lay annotators to reduce pixel-level annotation needs.
Adapted SAM for specific semantics and refined segmentation accuracy.

Main Results:

The all-in-SAM model significantly improved cell classification performance on diverse datasets.
Demonstrated robust performance even with varying annotation quality.
Validated through experiments on both in-house and public datasets.

Conclusions:

The molecular empowered all-in-SAM model reduces annotator workload and enhances pathology image analysis.
This approach increases the accessibility of precise biomedical image analysis, especially in resource-limited settings.
Advances medical diagnostics through automated pathology image analysis.