基于卷积神经网络的脑肿瘤分割的研究进展

Research Progress of Brain Tumor Segmentation Based on Convolutional Neural Network

基于卷积神经网络的脑肿瘤图像分割是近年来图像处理领域的研究热点。基于此现状,首先阐述了脑肿瘤图像分割的意义、研究现状以及将卷积神经网络应用于脑肿瘤图像分割的具体优势。然后,对二维卷积神经网络、三维卷积神经网络以及卷积神经网络的经典改进模型应用于脑肿瘤图像分割的研究进展进行了详细综述,总结了在多模态脑肿瘤分割挑战赛的数据集中进行训练的分割结果。最后,讨论了卷积神经网络在脑肿瘤核磁共振图像分割中的未来发展方向。

Brain tumor image segmentation based on convolutional neural network has become a research hotspot in the field of image processing in recent years. Based on this situation, the significance and research status of brain tumor image segmentation, and the specific advantages of applying convolutional neural network to brain tumor image segmentation are described. Then, the research progress of two-dimensional convolutional neural network, three-dimensional convolutional neural network and the classical improved model of convolutional neural network applied to brain tumor image segmentation is reviewed in detail, and the segmentation results of training in the dataset of multi-mode brain tumor segmentation challenge are summarized. Finally, the future development of convolutional neural networks in magnetic resonance imaging segmentation of brain tumors is discussed.