Computational neuroscience is concerned with simulating real neural systems in the hope of predicting brain workings and disorders, from sub-neuronal systems to network plasticity, as hypotheses to be tested later in real neural tissues, and hence to understand the principles governing them. Some ideas from this field can be used in artificial intelligence and other fields. Mimicking the central nervous system, and by extension creating various additional methods of computation such as artificial neural networks, machine learning, deep learning, or genetic algorithms, has led to the artificial intelligence field which aims to solve given problems in a flexible, intelligent, and learnable way. The advent of these fields has numerous biomedical applications, such as image processing and computer vision, machine learning and deep learning for the assessment of imaging and signal datasets, disease diagnostic systems, expert systems to offer and optimise treatment planning, brain-computer interface, smart prosthetic limbs, and many others.

Image processing is a subfield of digital signal processing and a vast set of techniques used to enhance or manipulate digital images to make them more practical in different ways for different purposes. Computer vision is a field of computer science concerned with understanding images, videos, or 3D volumes through extracting the desired features and attributes of images by various sets of algorithms and techniques.

The aim of this Special Issue is to publish research on clinical and paraclinical applications of artificial intelligence and computational neuroscience in computer vision, such as structural and functional brain imaging, microbiology, surgery, medical and dental radiography/tomography, etc. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Structural 3D brain imaging
• Functional 4D brain imaging
• Medical radiography
• Medical tomography and MRI
• 3D shape reconstruction and 3D printing
• Biomedical applications of computer vision in microbiology
• Dental radiography and tomography
• Automatic cephalometric tracing
• Artificial intelligence in biomedical computer vision
• Deep learning paradigms in biomedical machine vision
• Hardware devices dealing with brain signal analysis