Leveraging multimodal microscopy to optimize deep learning models for cell segmentation.

Area of Science:

• Computational Biology
• Biotechnology
• Machine Learning in Microscopy

Background:

• Deep learning enables automated cellular feature extraction from high-throughput microscopy.
• Diverse labeling strategies exist, including fluorescent markers and label-free techniques.
• Comparing labeling strategy effectiveness is challenging due to variations in training data channels.

Purpose of the Study:

• To develop a method for directly comparing different cell labeling strategies for deep learning models.
• To evaluate the performance of fluorescence-based versus label-free approaches in cell segmentation.
• To assess the impact of simulated labeling conditions on model generalizability.

Main Methods:

• Trained deep learning models using subimage stacks with sampled channels from larger, 'hyper-labeled' image stacks.
• Directly compared various fluorescence and label-free strategies on identical cells.
• Simulated diverse labeling conditions by varying channel composition in training subimage stacks.

Main Results:

• Fluorescence-based strategies generally yielded higher cell segmentation accuracies.
• Label-free models showed better accuracy when fluorescence labeling was inconsistent.
• Combining fluorescence channels with out-of-focus brightfield images leveraged strengths of both approaches.

Conclusions:

• Subimage stack training allows direct comparison of labeling strategies on identical cells.
• The developed method enhances model robustness by simulating varied labeling conditions.
• Hybrid approaches combining fluorescence and label-free data show promise for improved cell segmentation.