Deep learning -- promises for 3D nuclear imaging: a guide for biologists.

Area of Science:

• Cell Biology
• Biomedical Imaging
• Computational Biology

Background:

• The cell nucleus's shape and size regulation is crucial for understanding development, tissue function, disease, and aging.
• Microscopy and image analysis have historically been key tools for studying nuclear morphology.
• Deep learning, especially convolutional neural networks, shows significant promise for advancing quantitative nuclear analysis.

Purpose of the Study:

• To introduce cell biologists to the concepts and terminology of deep learning for nuclear image analysis.
• To highlight the importance of training image datasets and their accessibility.
• To review and classify deep learning methods suitable for 3D nuclear analysis based on usability for biologists.

Main Methods:

• Review of deep learning concepts and terminology relevant to image analysis.
• Emphasis on training data quality, creation, storage, and sharing.
• Classification of deep learning methods for 3D nuclear analysis based on biologist usability.

Main Results:

• Deep learning methods, particularly convolutional neural networks, offer powerful quantitative analysis of nuclear parameters.
• The quality and characteristics of training image datasets are critical for successful deep learning applications.
• Fewer than 12 out of over 150 reviewed deep learning methods are readily usable by biologists.

Conclusions:

• Deep learning presents a transformative approach to nuclear image analysis in cell biology.
• Best practices for creating, sharing, and utilizing deep learning methods are essential for broader adoption by biologists.
• Developing user-friendly deep learning tools is key to unlocking their full potential in nuclear research.