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Deep learning in mesoscale brain image analysis: A review.

Runze Chen¹, Min Liu², Weixun Chen¹

¹College of Electrical and Information Engineering, National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University, Changsha, 410082, China.

Computers in Biology and Medicine

|November 2, 2023

Summary

This summary is machine-generated.

Deep learning significantly enhances the analysis of mesoscale brain microscopy images, overcoming challenges like noise and complex morphology. This review covers deep learning applications in brain image processing, segmentation, and neuron analysis.

Keywords:

Brain imaging Deep learning Image analysis Image processing Light microscopy

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Area of Science:

Neuroscience
Computational Biology
Medical Imaging

Background:

Mesoscale microscopy images offer crucial insights into brain mechanisms.
Processing these images is challenging due to size, noise, complex morphology, and artifacts.

Purpose of the Study:

To review the applications of deep learning in processing and analyzing mesoscale brain microscopy images.
To highlight deep learning's effectiveness in overcoming image analysis challenges.

Main Methods:

Review of deep learning algorithms applied to brain microscopy image processing.
Focus on tasks including image synthesis, segmentation, object detection, and neuron reconstruction.

Main Results:

Deep learning excels at extracting relevant information from complex brain microscopy data.
Demonstrated superior performance in various image processing and analysis tasks.

Conclusions:

Deep learning is a powerful tool for advancing brain mesoscale image analysis.
Further research directions are discussed for improving these techniques.