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| 基于深度学习的脑肿瘤图像分割 | |
| 引用本文： | 刘琚,吴强,于璐跃,林枫茗.基于深度学习的脑肿瘤图像分割[J].山东大学学报(医学版),2020,58(8):42-49, 73. |
| 作者姓名： | 刘琚 吴强 于璐跃 林枫茗 |
| 作者单位： | 1. 山东大学信息科学与工程学院, 山东 青岛 266237 2. 山东大学脑与类脑科学研究院, 山东 济南 250012 |
| 基金项目： | 山东省自主创新及成果转化专项计划(2015ZDXX0801A01);山东省重点研发计划(2017CXGC1504);山东大学自主创新基金(自然科学专项);山东大学自主创新基金(2015QY001-05) |
| 摘 要： | 人工智能技术在计算机视觉与深度学习领域的应用逐渐增多，自动驾驶、无人机、医学临床诊疗等行业都需要基于深度学习的图像分割技术做支撑。本文对近年来脑肿瘤图像分割方法进行综述：首先介绍了图像分割的传统方法和基于深度学习的方法，然后概述了目前几种典型的针对脑肿瘤图像分割方法，描述其主要进展与可借鉴之处，总结了我们在基于深度学习的脑肿瘤图像分割方面的研究结果，并与典型方法的性能进行对比，最后讨论未来研究方向及面临的挑战。 |
| 关 键 词： | 人工智能 深度学习 图像分割 脑肿瘤图像 神经网络 |
| 收稿时间： | 2020-03-20 |
Brain tumor image segmentation based on deep learning techniques |
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| Ju LIU,Qiang WU,Luyue YU,Fengming LIN.Brain tumor image segmentation based on deep learning techniques[J].Journal of Shandong University:Health Sciences,2020,58(8):42-49, 73. | |
| Authors: | Ju LIU Qiang WU Luyue YU Fengming LIN |
| Institution: | 1. School of Information Science and Engineering, Shandong University, Qingdao 266237, Shandong, China 2. Institute of Brain and Brain-Inspired Science, Shandong University, Jinan 250012, Shandong, China |
| Abstract: | Artificial intelligence technology is widely applied in the field of computer vision and deep learning. Image segmentation technology based on deep learning is essential in industries such as autonomous driving, drones, and clinical diagnosis and treatment. This paper reviews the methods of brain tumor image segmentation, including the traditional methods of image segmentation and methods based on deep learning and some typical methods. The paper also compares our research advances with the typical methods and discusses future research directions and challenges. |
| Keywords: | Artificial intelligence Deep learning Image segmentation Brain tumor image Neural network |
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