Recent advances in deep learning have improved the segmentation accuracy of subcortical brain structures, which would be useful in neuroimaging studies of many neurological disorders. However, most existing deep learning based approaches in neuroimaging do not investigate the specific difficulties that exist in segmenting extremely small but important brain regions such as the subnuclei of the amygdala. To tackle this challenging task, we developed a dual-branch dilated residual 3D fully convolutional network with parallel convolutions to extract more global context and alleviate the class imbalance issue by maintaining a small receptive field that is just the size of the regions of interest (ROIs). We also conduct multi-scale feature fusion in both parallel and series to compensate the potential information loss during convolutions, which has been shown to be important for small objects. The serial feature fusion enabled by residual connections is further enhanced by a proposed top-down attention-guided refinement unit, where the high-resolution low-level spatial details are selectively integrated to complement the high-level but coarse semantic information, enriching the final feature representations. As a result, the segmentations resulting from our method are more accurate both volumetrically and morphologically, compared with other deep learning based approaches. To the best of our knowledge, this work is the first deep learning-based approach that targets the subregions of the amygdala. We also demonstrated the feasibility of using a cycle-consistent generative adversarial network (CycleGAN) to harmonize multi-site MRI data, and show that our method generalizes well to challenging traumatic brain injury (TBI) datasets collected from multiple centers. This appears to be a promising strategy for image segmentation for multiple site studies and increased morphological variability from significant brain pathology.

中文翻译：

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一种具有自上而下注意力引导细化的 3D 全卷积神经网络，用于准确、稳健地自动分割杏仁核及其亚核

深度学习的最新进展提高了皮层下大脑结构的分割精度，这将有助于许多神经系统疾病的神经影像学研究。然而，大多数现有的基于深度学习的神经影像学方法并没有研究在分割极小但重要的大脑区域（例如杏仁核的亚核）时存在的具体困难。为了解决这个具有挑战性的任务，我们开发了一个带有并行卷积的双分支扩张残差 3D 完全卷积网络，以通过保持一个与感兴趣区域 (ROI) 大小相同的小感受野来提取更多全局上下文并缓解类别不平衡问题）。我们还在并行和串行中进行多尺度特征融合，以补偿卷积过程中潜在的信息丢失，这已被证明对小物体很重要。通过提出的自上而下的注意力引导细化单元进一步增强了由残差连接实现的串行特征融合，其中选择性地集成高分辨率低级空间细节以补充高级但粗略的语义信息，丰富最终的特征表示。因此，与其他基于深度学习的方法相比，我们的方法产生的分割在体积和形态上都更加准确。据我们所知，这项工作是第一个针对杏仁核子区域的基于深度学习的方法。我们还证明了使用循环一致的生成对抗网络（CycleGAN）来协调多站点 MRI 数据的可行性，并表明我们的方法可以很好地推广到从多个中心收集的具有挑战性的创伤性脑损伤 (TBI) 数据集。这似乎是用于多部位研究的图像分割和增加来自显着脑病理的形态变异性的有前途的策略。