Zhao Xingzhong, Zhao Xing-Ming
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, China.
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, China; Research Institute of Intelligent Complex Systems, Fudan University, Shanghai 200433, China.
Methods. 2021 Aug;192:131-140. doi: 10.1016/j.ymeth.2020.09.007. Epub 2020 Sep 12.
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.
磁共振成像（MRI）是脑科学中最常用的技术之一，对于理解脑功能和神经精神疾病至关重要。然而，MRI的处理和分析并非易事，面临诸多挑战。近年来，深度学习在图像分析方面展现出优于传统机器学习方法的性能。在本次综述中，我们简要回顾了近期流行的深度学习方法及其在脑MRI分析中的应用。此外，还介绍了常用的脑MRI数据库和深度学习工具。我们讨论了不同方法的优缺点，并探讨了面临的挑战和未来的发展方向。
