In this study, we proposed and validated a multi-atlas and diffeomorphism guided 3D fully convolutional network (FCN) ensemble model (M-FCN) for segmenting brain anatomical regions of interest (ROIs) from structural magnetic resonance images (MRIs). A novel multi-atlas and diffeomorphism based encoding block and ROI patches with adaptive sizes were used. In the multi-atlas and diffeomorphism based encoding block, both MRI intensity profiles and expert priors from deformed atlases were encoded and fed to the proposed FCN. Utilizing patches with adaptive sizes enabled more efficient network training and testing. To incorporate both local and global contextual information of a specific ROI, we employed a long skip connection between the layer of the encoding block and the layer of the encoding-decoding block. To relieve over-fitting of the proposed FCN model on the training data, we adopted an ensemble strategy in the learning procedure. Systematic evaluations were performed on two brain MRI datasets, aiming respectively at segmenting 14 subcortical and ventricular structures and 54 whole-brain ROIs. Compared with two state-of-the-art segmentation methods including a multi-atlas based segmentation method and an existing 3D FCN segmentation model, the proposed method exhibited superior segmentation performance.

在这项研究中，我们提出并验证了一种多图谱和多态性引导的3D全卷积网络（FCN）集成模型（M-FCN），用于从结构磁共振图像（MRI）分割感兴趣的大脑解剖区域（ROI）。使用了一种新颖的基于多图集和变态的编码块和具有自适应大小的ROI补丁。在基于多图谱和多态性的编码块中，对来自变形图谱的MRI强度分布图和专家先验都进行了编码，并馈入了拟议的FCN。通过使用具有自适应大小的补丁程序，可以更有效地进行网络培训和测试。为了合并特定ROI的本地和全局上下文信息，我们在编码块层和编码解码块层之间采用了长跳连接。为了减轻建议的FCN模型在训练数据上的过度拟合，我们在学习过程中采用了集成策略。对两个脑部MRI数据集进行了系统评估，分别针对14个皮质下和心室结构以及54个全脑ROI。与基于多图集的分割方法和现有的3D FCN分割模型这两种最新的分割方法相比，该方法展现出了出色的分割性能。