论文标题
一种新型的3D多路径Densenet，用于改善术前多模式MR图像对胶质母细胞瘤的自动分割
A novel 3D multi-path DenseNet for improving automatic segmentation of glioblastoma on pre-operative multi-modal MR images
论文作者
论文摘要
卷积神经网络在自动医疗图像分割方面取得了出色的成果。在这项研究中，我们提出了一种新型的3D多路径Densenet，用于从四个多模式术前MR图像中产生精确的胶质母细胞瘤（GBM）肿瘤轮廓。我们假设，多路径体系结构可以比单条路径体系结构实现更准确的分割。这项研究包括258名GBM患者。每个患者都有四个MR图像（T1加权，对比增强的T1加权，T2加权和FLAIR），并手动分割了肿瘤轮廓。我们构建了一个3D多路径densenet，可以训练从四个MR图像中生成相应的GBM肿瘤轮廓。还建造了一个3D单路登机网供比较。这两个登录都是基于编码器架构的。将所有四个图像都串联并馈入单个路径densenet中的单个编码器路径，而每个输入图像在多路径densenet中都有其自己的编码器路径。将患者队列随机分为180例患者，验证39例患者和39例患者的验证。使用骰子相似系数（DSC），平均表面距离（ASD）和95％Hausdorff距离（HD95％）评估模型性能。进行了Wilcoxon签名的秩检验，以检查模型差异。单件path的Densenet的DSC为0.911 $ \ pm $ 0.060，ASD为1.3 $ \ pm $ 0.7毫米，HD95％的HD95％5.2 $ \ pm $ 7.1毫米7.1毫米，而多path densenet的DSC达到了0.922 $ \ pm $ 0.041 $ $ 0.5mm的DSC，ASD和1.1 $ 0.5毫米，ASD $ 0.5毫米， 3.9 $ \ pm $ 3.3毫米。所有Wilcoxon签名级测试的P值小于0.05。两种3D登录都与多模式MR图像的手动分割轮廓生成了GBM肿瘤轮廓。比单路径登森烯具有更准确的肿瘤分割。
Convolutional neural networks have achieved excellent results in automatic medical image segmentation. In this study, we proposed a novel 3D multi-path DenseNet for generating the accurate glioblastoma (GBM) tumor contour from four multi-modal pre-operative MR images. We hypothesized that the multi-path architecture could achieve more accurate segmentation than a single-path architecture. 258 GBM patients were included in this study. Each patient had four MR images (T1-weighted, contrast-enhanced T1-weighted, T2-weighted, and FLAIR) and the manually segmented tumor contour. We built a 3D multi-path DenseNet that could be trained to generate the corresponding GBM tumor contour from the four MR images. A 3D single-path DenseNet was also built for comparison. Both DenseNets were based on the encoder-decoder architecture. All four images were concatenated and fed into a single encoder path in the single-path DenseNet, while each input image had its own encoder path in the multi-path DenseNet. The patient cohort was randomly split into a training set of 180 patients, a validation set of 39 patients, and a testing set of 39 patients. Model performance was evaluated using the Dice similarity coefficient (DSC), average surface distance (ASD), and 95% Hausdorff distance (HD95%). Wilcoxon signed-rank tests were conducted to examine the model differences. The single-path DenseNet achieved a DSC of 0.911$\pm$0.060, ASD of 1.3$\pm$0.7 mm, and HD95% of 5.2$\pm$7.1 mm, while the multi-path DenseNet achieved a DSC of 0.922$\pm$0.041, ASD of 1.1$\pm$0.5 mm, and HD95% of 3.9$\pm$3.3 mm. The p-values of all Wilcoxon signed-rank tests were less than 0.05. Both 3D DenseNets generated GBM tumor contours in good agreement with the manually segmented contours from multi-modal MR images. The multi-path DenseNet achieved more accurate tumor segmentation than the single-path DenseNet.
