Title: Multi-Resolution 3D CNN for MRI Brain Tumor Segmentation and Survival Prediction Show Chinese title

Title: 多分辨率3D CNN用于MRI脑肿瘤分割和生存预测

Abstract: In this study, an automated three dimensional (3D) deep segmentation approach for detecting gliomas in 3D pre-operative MRI scans is proposed. Then, a classi-fication algorithm based on random forests, for survival prediction is presented. The objective is to segment the glioma area and produce segmentation labels for its different sub-regions, i.e. necrotic and the non-enhancing tumor core, the peri-tumoral edema, and enhancing tumor. The proposed deep architecture for the segmentation task encompasses two parallel streamlines with two different reso-lutions. One deep convolutional neural network is to learn local features of the input data while the other one is set to have a global observation on whole image. Deemed to be complementary, the outputs of each stream are then merged to pro-vide an ensemble complete learning of the input image. The proposed network takes the whole image as input instead of patch-based approaches in order to con-sider the semantic features throughout the whole volume. The algorithm is trained on BraTS 2019 which included 335 training cases, and validated on 127 unseen cases from the validation dataset using a blind testing approach. The proposed method was also evaluated on the BraTS 2019 challenge test dataset of 166 cases. The results show that the proposed methods provide promising segmentations as well as survival prediction. The mean Dice overlap measures of automatic brain tumor segmentation for validation set were 0.84, 0.74 and 0.71 for the whole tu-mor, core and enhancing tumor, respectively. The corresponding results for the challenge test dataset were 0.82, 0.72, and 0.70, respectively. The overall accura-cy of the proposed model for the survival prediction task is %52 for the valida-tion and %49 for the test dataset. Show Chinese abstract

Abstract: 在本研究中，提出了一种自动化的三维（3D）深度分割方法，用于检测三维术前MRI扫描中的胶质瘤。 然后，提出了一种基于随机森林的分类算法，用于生存预测。 目标是分割胶质瘤区域并为其不同子区域生成分割标签，即坏死区和非增强肿瘤核心、肿瘤周围水肿以及增强肿瘤。 用于分割任务的所提出的深度架构包含两个并行的数据流，具有两种不同的分辨率。 一个深度卷积神经网络用于学习输入数据的局部特征，而另一个则用于对整个图像进行全局观察。 由于它们被认为是互补的，每个流的输出随后被合并，以提供对输入图像的集成完整学习。 所提出的网络将整个图像作为输入，而不是基于补丁的方法，以便考虑整个体积中的语义特征。 该算法在BraTS 2019数据集上进行训练，该数据集包括335个训练病例，并使用盲测试方法在验证数据集的127个未见过的病例上进行验证。 所提出的方法还在BraTS 2019挑战测试数据集的166个病例上进行了评估。 结果表明，所提出的方法提供了有前景的分割以及生存预测。 对于验证集的自动脑肿瘤分割的平均Dice重叠度量分别为0.84、0.74和0.71，分别对应整个肿瘤、核心和增强肿瘤。 对于挑战测试数据集的相应结果分别为0.82、0.72和0.70。 所提出模型在生存预测任务中的总体准确率在验证集中为%52，在测试数据集中为%49。