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A Deep Learning Multimodal Fusion-Based Method for Cell and Nucleus Segmentation.

Bin Shen, Zhen Gu, Jiale Zhou

IEEE Transactions on Medical Imaging

|July 25, 2025

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This study introduces a deep learning multimodal fusion method for cell and nucleus segmentation, overcoming limitations of scarce annotated data. The approach effectively segments cells without retraining, showing superior performance in experiments.

Area of Science:

Biomedical image analysis
Deep learning applications
Cellular imaging

Background:

Supervised deep learning excels in cell and nucleus segmentation but requires extensive annotated data.
High-quality annotated cellular image datasets are scarce, limiting supervised model performance.
Existing methods often require retraining for new datasets.

Purpose of the Study:

To propose a novel deep learning multimodal fusion method for cell and nucleus segmentation.
To address the challenge of limited annotated data in cellular image analysis.
To develop a method that performs segmentation without the need for retraining on new data.

Main Methods:

A three-module framework: segmentation fundamental module, multimodal prompter module, and object output module.

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Last Updated: Sep 13, 2025

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Leveraging pretrained models trained on natural imagery for segmentation capabilities.

Utilizing a multimodal prompter module with data fusion techniques for image and textual information integration.

Main Results:

The proposed method achieves superior performance in cell and nucleus segmentation tasks compared to existing methods.
Experimental validation confirms the effectiveness of the multimodal fusion approach.
The method demonstrates the ability to perform segmentation without retraining.

Conclusions:

The developed deep learning multimodal fusion method offers a robust solution for cell and nucleus segmentation with limited data.
The approach shows significant promise for future applications, including cell tracking.
Multimodal fusion effectively overcomes the constraints of single-modality approaches in cellular image analysis.