关键词: 卷积神经网络, 脑肿瘤, 多尺度, 多任务, 多视图

Abstract:

The automatic segmentation of brain tumors plays an important role in brain tumor diagnosis, surgical planning and treatment evaluation. However, it still faces huge challenges due to the high variability and fuzzy boundaries of tumors, as well as limited data and class imbalance. At present, most methods rely on manual work, which leads to time-consuming, labor-intensive and subjective influence. It is of great research significance to seek an efficient automatic segmentation method. In order to understand such methods, the research background, significance and difficulties of brain tumor segmentation are introduced, and its development process is summarized. Then the Convolutional Neural Network（CNN） based on brain tumor segmentation is described in detail from two aspects of data and structure optimization, as well as commonly used data set and indicators are introduced. Finally, the performance of the top-ranked algorithms in the BraTs challenge from 2017 to 2019 are analyzed, and the development trend of convolutional neural networks in brain tumor segmentation is discussed.

Key words: Convolutional Neural Network（CNN）, brain tumor, multi-scale, multi-task, multi-view