Perspectives: Comparison of deep learning segmentation models on biophysical and biomedical data

Area of Science:

• Biophysics
• Computational Biology
• Machine Learning in Science

Background:

• Deep learning automates tasks like image segmentation in biophysics.
• Selecting the optimal deep learning architecture is challenging due to numerous options.
• Biophysics experiments often yield small training datasets, complicating model selection.

Purpose of the Study:

• To comprehensively compare common deep learning architectures for image segmentation in biophysics.
• To establish criteria for optimal model performance based on dataset size and experimental context.
• To provide practical guidelines for researchers choosing deep learning models for biophysical applications.

Main Methods:

• Focused on image segmentation using typical small biophysics training datasets.
• Compared four prominent deep learning architectures: convolutional neural networks (CNNs), U-Nets, vision transformers (ViTs), and vision state space models (VSSMs).
• Evaluated model performance under varying conditions relevant to biophysical research.

Main Results:

• Identified specific conditions under which each architecture (CNNs, U-Nets, ViTs, VSSMs) demonstrates superior performance for image segmentation.
• Established performance benchmarks for different deep learning models on small biophysics datasets.
• Provided comparative analysis highlighting the strengths and weaknesses of each architecture.

Conclusions:

• The choice of deep learning architecture significantly impacts image segmentation performance in biophysics.
• This comparison offers practical guidance for researchers to select the most effective model for their specific biophysics applications and dataset characteristics.
• The findings facilitate more efficient and accurate automation of biophysical research tasks through informed deep learning model selection.