With the development of advanced medical imaging techniques, a huge amount of medical images have been produced in various healthcare institutes and hospitals. Especially, there is a growing research interest on a more multidisciplinary approach to investigate brain structure or function in living humans or animals. In order to better interpret brain images, there is an increasing demand to introduce artificial intelligence methods such as data-enabled intelligence, expert systems, robotics and perception, and evolutionary computation to automatically exploit useful information besides visual features. It should be pointed out that brain images themselves exhibit several distinguishing features that add to the difficulties in their analysis. In recent years, there have been many new research achievements in each aspect of artificial intelligence for brain images analysis. This Research Topic seeks original contributions which address the challenges of artificial intelligence for neuroimaging analysis and welcomes researchers in this field to share their experiences and new research achievements.
Topics of this collection include (but are not limited to) artificial intelligence methods with their applications in:
• Computer-aided detection/diagnosis
• Classification and analysis of anatomical structures, lesions and lesion subtypes/stages
• Biomedical image segmentation
• Multiple modality fusion
• Medical image reconstruction
• Pathology image analysis
• Neuro-degeneration
• Neuro-development
• Neurological disorders
• Treatment evaluation
• Multi-site studies
Artificial intelligence methods include (but are not limited to):
• Data-enabled Intelligence
• Expert systems
• Knowledge representation
• Robotics and perception
• Reinforcement learning
• Evolutionary computation
Topic editor Tong Tong is employed by company Imperial Vision Technology. All other topic editors declare no competing interests with regards to the Research Topic subject.
With the development of advanced medical imaging techniques, a huge amount of medical images have been produced in various healthcare institutes and hospitals. Especially, there is a growing research interest on a more multidisciplinary approach to investigate brain structure or function in living humans or animals. In order to better interpret brain images, there is an increasing demand to introduce artificial intelligence methods such as data-enabled intelligence, expert systems, robotics and perception, and evolutionary computation to automatically exploit useful information besides visual features. It should be pointed out that brain images themselves exhibit several distinguishing features that add to the difficulties in their analysis. In recent years, there have been many new research achievements in each aspect of artificial intelligence for brain images analysis. This Research Topic seeks original contributions which address the challenges of artificial intelligence for neuroimaging analysis and welcomes researchers in this field to share their experiences and new research achievements.
Topics of this collection include (but are not limited to) artificial intelligence methods with their applications in:
• Computer-aided detection/diagnosis
• Classification and analysis of anatomical structures, lesions and lesion subtypes/stages
• Biomedical image segmentation
• Multiple modality fusion
• Medical image reconstruction
• Pathology image analysis
• Neuro-degeneration
• Neuro-development
• Neurological disorders
• Treatment evaluation
• Multi-site studies
Artificial intelligence methods include (but are not limited to):
• Data-enabled Intelligence
• Expert systems
• Knowledge representation
• Robotics and perception
• Reinforcement learning
• Evolutionary computation
Topic editor Tong Tong is employed by company Imperial Vision Technology. All other topic editors declare no competing interests with regards to the Research Topic subject.
