Accurate segmentation of multiple gliomas from multimodal MRI is a prerequisite for many precision medical procedures. To effectively use the characteristics of glioma MRI and im-prove the segmentation accuracy, we proposes a multi-Dice loss function structure and used pre-experiments to select the good hyperparameters (i.e. data dimension, image fusion step, and the implementation of loss function) to construct a 3D full convolution DenseNet-based image feature learning network. This study included 274 segmented training sets of glioma MRI and 110 test sets without segmentation. After grayscale normalization of the image, the 3D image block was extracted as a network input, and the network output used the image block fusion method to obtain the final segmentation result. The proposed structure improved the accuracy of glioma segmentation compared to a general structure. In the on-line assessment of the open BraTS2015 data set, the Dice values for the entire tumor area, tumor core area, and enhanced tumor area were 0.85, 0.71, and 0.63, respectively.
从多模态MRI中对多个脑胶质瘤区域进行精确分割是不少精准医疗步骤的前提。为了有效针对脑胶质瘤MRI的特性和提升其分割精度，本文提出了多Dice损失函数结构，并采用预实验选择良好的超参数（数据维度、图像融合步长、损失函数的实现形式）构建一个基于三维全卷积DenseNet的图像特征学习网络。本研究包含了脑胶质瘤MRI的274个已分割训练集和110个未提供分割的测试集。图像进行灰度归一化后提取三维图像块作为网络输入，网络输出利用图像块融合方法得到最终的分割结果。相比通用的结构，推荐的结构提高了脑胶质瘤的分割精度。在公开的BraTS2015数据集上进行在线的评估中，整个肿瘤区、肿瘤核心区和增强肿瘤区的Dice值分别为0.85、0.71、0.63。