Auto-Context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging.
Auto-Context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging.
复制标题
DOI：
10.1109/tmi.2017.2721362
复制
发表时间：
2017-11
影响因子：
10.6
通讯作者：
Gholipour A
中科院分区：
文献类型：
--
作者：
Mohseni Salehi SS;Erdogmus D;Gholipour A
脑提取或全脑分割是许多神经图像分析管道中重要的第一步。因此，脑提取的准确性和鲁棒性对整个脑分析过程的准确性至关重要。最先进的脑提取技术严重依赖于脑地图集之间对齐或配准的准确性，以及查询脑解剖结构和/或对图像几何形状进行假设；因此，当这些假设不成立或图像配准失败时，成功率有限。为了设计一种准确的、基于学习的、几何无关的、无配准的大脑提取工具，本研究提出了一种基于自上下文卷积神经网络（CNN）的技术，该技术通过不同窗口大小的二维补丁学习图像的局部和全局特征。我们考虑了两种不同的架构：1)基于三个不同方向（轴向，冠状和矢状）的三个并行2D卷积路径的体素方法，该方法隐式学习3D图像信息，而不需要计算昂贵的3D卷积；2)基于U-net架构的全卷积网络。由网络生成的后验概率图作为上下文信息与原始图像补丁一起迭代使用，学习大脑的局部形状和连通性，并从非脑组织中提取。在两个公开的基准数据集LPBA40和OASIS上，我们从我们的cnn中获得的脑提取结果优于最近报道的文献结果，其中我们获得的Dice重叠系数分别为97.73%和97.62%。通过我们的自动上下文算法实现了显著的改进。此外，我们评估了我们的算法在重建胎儿脑磁共振成像（MRI）数据集中提取任意方向胎儿脑的挑战性问题中的性能。在这个应用中，我们的体向自动上下文CNN表现得比其他方法好得多（Dice系数：95.97%），而其他方法由于MRI中胎儿大脑的非标准方向和几何形状而表现不佳。通过训练，我们的方法可以在具有挑战性的应用中提供准确的大脑提取。这反过来又可以减少分割任务中与图像配准相关的问题。
Brain extraction or whole brain segmentation is an important first step in many of the neuroimage analysis pipelines. The accuracy and robustness of brain extraction, therefore, is crucial for the accuracy of the entire brain analysis process. State-of-the-art brain extraction techniques rely heavily on the accuracy of alignment or registration between brain atlases and query brain anatomy, and/or make assumptions about the image geometry; therefore have limited success when these assumptions do not hold or image registration fails. With the aim of designing an accurate, learning-based, geometry-independent and registration-free brain extraction tool in this study, we present a technique based on an auto-context convolutional neural network (CNN), in which intrinsic local and global image features are learned through 2D patches of different window sizes. We consider two different architectures: 1) a voxelwise approach based on three parallel 2D convolutional pathways for three different directions (axial, coronal, and sagittal) that implicitly learn 3D image information without the need for computationally expensive 3D convolutions, and 2) a fully convolutional network based on the U-net architecture. Posterior probability maps generated by the networks are used iteratively as context information along with the original image patches to learn the local shape and connectedness of the brain to extract it from non-brain tissue. The brain extraction results we have obtained from our CNNs are superior to the recently reported results in the literature on two publicly available benchmark datasets, namely LPBA40 and OASIS, in which we obtained Dice overlap coefficients of 97.73% and 97.62%, respectively. Significant improvement was achieved via our auto-context algorithm. Furthermore, we evaluated the performance of our algorithm in the challenging problem of extracting arbitrarily-oriented fetal brains in reconstructed fetal brain magnetic resonance imaging (MRI) datasets. In this application our voxelwise auto-context CNN performed much better than the other methods (Dice coefficient: 95.97%), where the other methods performed poorly due to the non-standard orientation and geometry of the fetal brain in MRI. Through training, our method can provide accurate brain extraction in challenging applications. This in-turn may reduce the problems associated with image registration in segmentation tasks.