1. Deep learning of brain magnetic resonance images: A brief review
- Author
-
Xing-Ming Zhao and Xingzhong Zhao
- Subjects
Computer science ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Machine learning ,computer.software_genre ,General Biochemistry, Genetics and Molecular Biology ,03 medical and health sciences ,Deep Learning ,Image Processing, Computer-Assisted ,medicine ,Brain mri ,Brain magnetic resonance imaging ,Molecular Biology ,Brain function ,030304 developmental biology ,0303 health sciences ,medicine.diagnostic_test ,business.industry ,Deep learning ,030302 biochemistry & molecular biology ,Brain ,Magnetic resonance imaging ,Magnetic Resonance Imaging ,Computer-aided diagnosis ,Artificial intelligence ,business ,computer - Abstract
Magnetic resonance imaging (MRI) is one of the most popular techniques in brain science and is important for understanding brain function and neuropsychiatric disorders. However, the processing and analysis of MRI is not a trivial task with lots of challenges. Recently, deep learning has shown superior performance over traditional machine learning approaches in image analysis. In this survey, we give a brief review of the recent popular deep learning approaches and their applications in brain MRI analysis. Furthermore, popular brain MRI databases and deep learning tools are also introduced. The strength and weaknesses of different approaches are addressed, and challenges as well as future directions are also discussed.
- Published
- 2021