DeepNAT：用于神经解剖学分割的深度卷积神经网络
DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy
摘要：
我们介绍DeepNAT，一种3D深度卷积神经网络，用于在T1加权磁共振图像中自动分割NeuroAnaTomy。DeepNAT是一种端到端的基于学习的大脑分割方法，它联合学习抽象特征表示和多类分类。我们提出了一种基于3D补丁的方法，在这里我们不仅预测补丁的中心体素，而且预测邻居，这被称为多任务学习。为了解决类别不平衡问题，我们分层安排了两个网络，第一个将前景与背景分开，第二个在前景上识别25个大脑结构。由于斑块缺乏空间背景，我们用坐标来增强它们。为此，我们引入了一种新的内在参数化的脑容量，形成的拉普拉斯-贝尔特拉米算子的本征函数。作为网络架构，我们使用三个卷积层，包括池化、批量归一化和非线性，然后是具有丢弃的全连接层。最终的分割是从具有3D全连接条件随机场的网络的概率输出推断的，这确保了接近体素之间的标签一致性。网络中大约270万个参数是用随机梯度下降学习的。我们的研究结果表明，DeepNAT与最先进的方法相比毫不逊色。最后，纯粹基于学习的方法可能具有很高的潜力，可以通过在目标应用程序上使用小的训练样本微调预训练的网络来适应年轻，年老或患病的大脑，其中具有手动注释的较大数据集的可用性可能会提高未来的整体分割准确性。
英文摘要：
We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstract feature representation and a multi-class classification. We propose a 3D patch-based approach, where we do not only predict the center voxel of the patch but also neighbors, which is formulated as multi-task learning. To address a class imbalance problem, we arrange two networks hierarchically, where the first one separates foreground from background, and the second one identifies 25 brain structures on the foreground. Since patches lack spatial context, we augment them with coordinates. To this end, we introduce a novel intrinsic parameterization of the brain volume, formed by eigenfunctions of the Laplace-Beltrami operator. As network architecture, we use three convolutional layers with pooling, batch normalization, and non-linearities, followed by fully connected layers with dropout. The final segmentation is inferred from the probabilistic output of the network with a 3D fully connected conditional random field, which ensures label agreement between close voxels. The roughly 2.7 million parameters in the network are learned with stochastic gradient descent. Our results show that DeepNAT compares favorably to state-of-the-art methods. Finally, the purely learning-based method may have a high potential for the adaptation to young, old, or diseased brains by fine-tuning the pre-trained network with a small training sample on the target application, where the availability of larger datasets with manual annotations may boost the overall segmentation accuracy in the future.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
