论文标题
上下文意识到3D UNET用于脑肿瘤细分
Context Aware 3D UNet for Brain Tumor Segmentation
论文作者
论文摘要
深度卷积神经网络（CNN）在医学图像分析方面取得了显着的性能。 UNET是用于医学成像任务（包括脑肿瘤分割）的3D CNN体系结构性能的主要来源。 UNET体系结构中的跳过连接来自编码器和解码器路径的特征，以从图像数据中提取多文字信息。多尺度特征在脑肿瘤分割中起着至关重要的作用。但是，功能的使用有限可以降低UNET方法进行分割的性能。在本文中，我们提出了一种修改的UNET结构，用于脑肿瘤分割。在提出的体系结构中，我们使用编码器和解码器路径中的密集连接块来从特征可重复使用的概念中提取多上下文信息。此外，剩余的构成块（RIB）用于通过合并不同内核大小的特征来提取本地和全局信息。我们验证了有关多模式脑肿瘤分割挑战（BRAT）2020测试数据集的拟议结构。整个肿瘤（WT），肿瘤核心（TC）和增强肿瘤（ET）的骰子（DSC）分别分别为89.12％，84.74％和79.12％。
Deep convolutional neural network (CNN) achieves remarkable performance for medical image analysis. UNet is the primary source in the performance of 3D CNN architectures for medical imaging tasks, including brain tumor segmentation. The skip connection in the UNet architecture concatenates features from both encoder and decoder paths to extract multi-contextual information from image data. The multi-scaled features play an essential role in brain tumor segmentation. However, the limited use of features can degrade the performance of the UNet approach for segmentation. In this paper, we propose a modified UNet architecture for brain tumor segmentation. In the proposed architecture, we used densely connected blocks in both encoder and decoder paths to extract multi-contextual information from the concept of feature reusability. In addition, residual-inception blocks (RIB) are used to extract the local and global information by merging features of different kernel sizes. We validate the proposed architecture on the multi-modal brain tumor segmentation challenge (BRATS) 2020 testing dataset. The dice (DSC) scores of the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) are 89.12%, 84.74%, and 79.12%, respectively.
