Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA.
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA.
Neuroimage. 2021 Feb 15;227:117649. doi: 10.1016/j.neuroimage.2020.117649. Epub 2020 Dec 15.
As non-human primates, macaques have a close phylogenetic relationship to human beings and have been proven to be a valuable and widely used animal model in human neuroscience research. Accurate skull stripping (aka. brain extraction) of brain magnetic resonance imaging (MRI) is a crucial prerequisite in neuroimaging analysis of macaques. Most of the current skull stripping methods can achieve satisfactory results for human brains, but when applied to macaque brains, especially during early brain development, the results are often unsatisfactory. In fact, the early dynamic, regionally-heterogeneous development of macaque brains, accompanied by poor and age-related contrast between different anatomical structures, poses significant challenges for accurate skull stripping. To overcome these challenges, we propose a fully-automated framework to effectively fuse the age-specific intensity information and domain-invariant prior knowledge as important guiding information for robust skull stripping of developing macaques from 0 to 36 months of age. Specifically, we generate Signed Distance Map (SDM) and Center of Gravity Distance Map (CGDM) based on the intermediate segmentation results as guidance. Instead of using local convolution, we fuse all information using the Dual Self-Attention Module (DSAM), which can capture global spatial and channel-dependent information of feature maps. To extensively evaluate the performance, we adopt two relatively-large challenging MRI datasets from rhesus macaques and cynomolgus macaques, respectively, with a total of 361 scans from two different scanners with different imaging protocols. We perform cross-validation by using one dataset for training and the other one for testing. Our method outperforms five popular brain extraction tools and three deep-learning-based methods on cross-source MRI datasets without any transfer learning.
作为非人类灵长类动物，猕猴与人类具有密切的系统发育关系，已被证明是人类神经科学研究中一种有价值且广泛应用的动物模型。准确的脑磁共振成像（MRI）颅骨剥离（又名脑提取）是神经影像学分析猕猴的关键前提。目前大多数颅骨剥离方法可以为人脑提供满意的结果，但应用于猕猴脑时，特别是在早期脑发育期间，结果往往不尽如人意。事实上，猕猴脑的早期动态、区域异质性发育，伴随着不同解剖结构之间对比度差且随年龄变化，这给准确的颅骨剥离带来了重大挑战。为了克服这些挑战，我们提出了一个全自动框架，有效地融合了特定年龄的强度信息和域不变的先验知识，作为从 0 到 36 月龄发育猕猴进行稳健颅骨剥离的重要指导信息。具体来说，我们基于中间分割结果生成有符号距离图（Signed Distance Map，SDM）和重心距离图（Center of Gravity Distance Map，CGDM）作为指导。我们没有使用局部卷积，而是使用双自注意模块（Dual Self-Attention Module，DSAM）融合所有信息，该模块可以捕获特征图的全局空间和通道相关信息。为了广泛评估性能，我们分别采用来自恒河猴和食蟹猴的两个相对较大的具有挑战性的 MRI 数据集，总共有来自两个不同成像协议的不同扫描仪的 361 次扫描。我们通过使用一个数据集进行训练和另一个数据集进行测试来进行交叉验证。我们的方法在没有任何迁移学习的情况下，在交叉源 MRI 数据集上优于五种流行的脑提取工具和三种基于深度学习的方法。
