Perspectives: Comparison of Deep Learning Segmentation Models on Biophysical and Biomedical Data

Area of Science:

• Biophysics
• Computational Biology
• Machine Learning

Background:

• Deep learning automates biophysics tasks like image segmentation.
• Selecting optimal deep learning architectures is challenging due to numerous options.

Purpose of the Study:

• To compare common deep learning architectures for image segmentation in biophysics.
• To provide practical guidelines for model selection with small training datasets.

Main Methods:

• Comparative analysis of four architectures: Convolutional Neural Networks (CNNs), U-Nets, Vision Transformers (ViTs), and Vision State Space Models (VSSMs).
• Evaluation focused on performance with limited biophysics experimental data.

Main Results:

• Established criteria for optimal model performance based on dataset characteristics.
• Identified specific conditions where each architecture (CNNs, U-Nets, ViTs, VSSMs) excels in image segmentation.

Conclusions:

• Offers practical guidance for biophysics researchers in choosing appropriate deep learning models.
• Facilitates efficient and effective application of deep learning for image segmentation in biophysics research.