# Super-resolution microscopy and deep learning methods: what can they bring to neuroscience: from neuron to 3D spine segmentation

## TL;DR

This review explores how advanced microscopy and deep learning can improve understanding of neuronal structures and their changes in diseases.

## Contribution

The paper provides an accessible overview of recent super-resolution microscopy and deep learning tools for analyzing neuronal structures.

## Key findings

- Super-resolution microscopy techniques like SIM, STED, and STORM enable detailed visualization of neuronal structures.
- Deep learning methods offer new approaches for segmenting complex neuronal features like dendritic spines.
- These technologies can help study structural changes in disorders like Parkinson’s and Alzheimer’s diseases.

## Abstract

In recent years, advances in microscopy and the development of novel fluorescent probes have significantly improved neuronal imaging. Many neuropsychiatric disorders are characterized by alterations in neuronal arborization, neuronal loss—as seen in Parkinson’s disease—or synaptic loss, as in Alzheimer’s disease. Neurodevelopmental disorders can also impact dendritic spine morphogenesis, as observed in autism spectrum disorders and schizophrenia. In this review, we provide an overview of the various labeling and microscopy techniques available to visualize neuronal structure, including dendritic spines and synapses. Particular attention is given to available fluorescent probes, recent technological advances in super-resolution microscopy (SIM, STED, STORM, MINFLUX), and segmentation methods. Aimed at biologists, this review presents both classical segmentation approaches and recent tools based on deep learning methods, with the goal of remaining accessible to readers without programming expertise.