关键词: 分割, 脑胶质瘤, 3D卷积神经网络, 注意力机制, 超参数损失函数

Abstract:

A 3D Convolutional Neural Network（CNN） algorithm combining attention model is proposed to improve the accuracy of glioma segmentation. The image blocks of three different scales are inputted, and then after 9 convolutional layers and one classification layer, the 3 different classification results are obtained. The output results are obtained through multiply the classification results with the weights learned of the attention mechanism and add them pixel by pixel. In addition, the algorithm uses a hyperparameter loss function that combines the Dice loss function with the Focal loss function to improve the segmentation results. Experiments show that the Dice coefficient of the algorithm reaches 95.31%, 80.12%, and 82.25% in the whole region, core region and enhanced region, respectively. Compared with the existing glioma segmentation algorithm deepmedic, the Dice coefficient in the whole region, core region and enhanced region increased by 3%, 2%, and 6%, respectively. It has important clinical significance in the segmentation of Glioblastoma multiforme.

Key words: segmentation, glioblastoma multiforme, 3D convolutional neural network, attention, hyperparameter loss function