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Cellular data extraction from multiplexed brain imaging data using self-supervised Dual-loss Adaptive Masked

Son T Ly¹, Bai Lin¹, Hung Q Vo¹

¹Department of Electrical and Computer Engineering, University of Houston, TX 77204, USA.

Artificial Intelligence in Medicine

|April 2, 2024

Summary

This study introduces Dual-Loss Adaptive Masked Autoencoder (DAMA), a self-supervised method for brain cell analysis. DAMA effectively learns features from multiplexed immunofluorescence images, improving cell detection and segmentation without extensive manual annotation.

Keywords:

Multiplexed immunofluorescence image analysis Self-supervised learning

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Area of Science:

Neuroscience
Computational Biology
Biomedical Imaging

Background:

Accurate cell detection and segmentation are crucial for understanding brain function and accelerating drug discovery.
Deep learning methods show promise for cell image analysis, but require extensive annotated data, which is costly and time-consuming to generate.
Multiplexed immunofluorescence imaging generates rich datasets for brain studies, but analyzing them at scale remains challenging.

Purpose of the Study:

To develop a novel self-supervised learning method for extracting features from multiplexed immunofluorescence brain images.
To overcome the limitations of supervised learning approaches that require significant manual annotation by skilled biologists.
To improve the efficiency and accuracy of large-scale cell detection, segmentation, and classification in brain histology.

Main Methods:

Introduced Dual-Loss Adaptive Masked Autoencoder (DAMA), a self-supervised learning framework.
DAMA utilizes an objective function that minimizes conditional entropy in pixel-level reconstruction and feature-level regression.
Employed a novel adaptive mask sampling strategy to maximize mutual information, outperforming random masking in learning brain cell data.

Main Results:

DAMA features enabled superior performance in cell detection, segmentation, and classification on multiplexed immunofluorescence brain images.
The method demonstrated effectiveness even with limited annotations.
Experiments on the TissueNet dataset confirmed DAMA's generalizability across different tissue types and imaging platforms.

Conclusions:

DAMA represents a significant advancement in self-supervised learning for multiplexed immunofluorescence brain image analysis.
The developed framework reduces the reliance on manual annotation, making large-scale cell phenotyping more accessible.
The publicly available code facilitates further research and application in neuroscience and drug development.