论文标题
ACENET：神经解剖学分割的解剖上下文编码网络
ACEnet: Anatomical Context-Encoding Network for Neuroanatomy Segmentation
论文作者
论文摘要
磁共振（MR）扫描的大脑结构分割在脑形态定量中起重要作用。由于3D深度学习模型遭受了高计算成本的影响，因此2D深度学习方法的计算效率受到青睐。但是，现有的2D深度学习方法没有能够有效捕获实现精确大脑结构细分所需的3D空间上下文信息。 In order to overcome this limitation, we develop an Anatomical Context-Encoding Network (ACEnet) to incorporate 3D spatial and anatomical contexts in 2D convolutional neural networks (CNNs) for efficient and accurate segmentation of brain structures from MR scans, consisting of 1) an anatomical context encoding module to incorporate anatomical information in 2D CNNs and 2) a spatial context encoding模块将3D图像信息集成在2D CNN中。此外，采用了一个头骨剥离模块来指导2D CNN参加大脑。在三个基准数据集上进行的广泛实验表明，与最新的脑结构分割方法相比，我们的方法在计算效率和分割精度方面实现了有希望的性能。
Segmentation of brain structures from magnetic resonance (MR) scans plays an important role in the quantification of brain morphology. Since 3D deep learning models suffer from high computational cost, 2D deep learning methods are favored for their computational efficiency. However, existing 2D deep learning methods are not equipped to effectively capture 3D spatial contextual information that is needed to achieve accurate brain structure segmentation. In order to overcome this limitation, we develop an Anatomical Context-Encoding Network (ACEnet) to incorporate 3D spatial and anatomical contexts in 2D convolutional neural networks (CNNs) for efficient and accurate segmentation of brain structures from MR scans, consisting of 1) an anatomical context encoding module to incorporate anatomical information in 2D CNNs and 2) a spatial context encoding module to integrate 3D image information in 2D CNNs. In addition, a skull stripping module is adopted to guide the 2D CNNs to attend to the brain. Extensive experiments on three benchmark datasets have demonstrated that our method achieves promising performance compared with state-of-the-art alternative methods for brain structure segmentation in terms of both computational efficiency and segmentation accuracy.
