Focus on the inconsistency of the shape, location and size of brain glioma, a dual-channel 3-dimensional (3D) densely connected network is proposed to automatically segment brain glioma tumor on magnetic resonance images. Our method is based on a 3D convolutional neural network frame, and two convolution kernel sizes are adopted in each channel to extract multi-scale features in different scales of receptive fields. Then we construct two densely connected blocks in each pathway for feature learning and transmission. Finally, the concatenation of two pathway features was sent to classification layer to classify central region voxels to segment brain tumor automatically. We train and test our model on open brain tumor segmentation challenge dataset, and we also compared our results with other models. Experimental results show that our algorithm can segment different tumor lesions more accurately. It has important application value in the clinical diagnosis and treatment of brain tumor diseases.
针对脑胶质瘤形状、位置及大小的不一致性，本文提出了一种基于双通道三维密集连接网络的脑胶质瘤核磁共振成像（MRI）自动分割算法。该算法基于三维卷积神经网络，在两个通道采用不同大小卷积核，从而在不同尺度感受野下提取多尺度特征，并构造各自的密集连接块进行特征学习与传递，通过特征结联后输入到分类层进行目标体素分类，最终实现脑胶质瘤的自动分割。为了验证本文算法的实用性，本文采用公开的脑肿瘤分割挑战赛数据集对网络进行训练与验证，并将得到的结果与其他脑胶质瘤分割方法比较。实验结果表明，本文所提出的算法能够更准确地分割出不同的肿瘤病变区域，在临床脑肿瘤疾病诊断中具有一定的应用价值。.
Keywords: brain glioma segmentation; densely connected block; magnetic resonance imaging; three-dimensional convolutional neural network.