Automatic segmentation of the hippocampus from 3D magnetic resonance imaging mostly relied on multi-atlas registration methods. In this work, we exploit recent advances in deep learning to design and implement a fully automatic segmentation method, offering both superior accuracy and fast result. The proposed method is based on deep Convolutional Neural Networks (CNNs) and incorporates distinct segmentation and error correction steps. Segmentation masks are produced by an ensemble of three independent models, operating with orthogonal slices of the input volume, while erroneous labels are subsequently corrected by a combination of Replace and Refine networks. We explore different training approaches and demonstrate how, in CNN-based segmentation, multiple datasets can be effectively combined through transfer learning techniques, allowing for improved segmentation quality. The proposed method was evaluated using two different public datasets and compared favorably to existing methodologies. In the EADC-ADNI HarP dataset, the correspondence between the method’s output and the available ground truth manual tracings yielded a mean Dice value of 0.9015, while the required segmentation time for an entire MRI volume was 14.8 seconds. In the MICCAI dataset, the mean Dice value increased to 0.8835 through transfer learning from the larger EADC-ADNI HarP dataset.

中文翻译：

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通过深度卷积神经网络集成和转移学习进行快速，精确的海马分割。

通过3D磁共振成像对海马进行自动分割主要依靠多图谱配准方法。在这项工作中，我们利用深度学习的最新进展来设计和实现全自动分割方法，从而提供卓越的准确性和快速的结果。所提出的方法基于深度卷积神经网络（CNN），并结合了不同的分割和纠错步骤。分割蒙版是由三个独立模型的集合产生的，使用输入体积的正交切片进行操作，而错误的标签随后将通过“替换”和“细化”网络的组合进行纠正。我们探索了不同的训练方法，并演示了如何在基于CNN的细分中通过转移学习技术有效地组合多个数据集，可以提高细分质量。使用两个不同的公共数据集对提出的方法进行了评估，并与现有方法进行了比较。在EADC-ADNI HarP数据集中，该方法的输出与可用的地面实况手动跟踪之间的对应关系产生的平均Dice值为0.9015，而整个MRI体积所需的分割时间为14.8秒。在MICCAI数据集中，通过从较大的EADC-ADNI HarP数据集中进行转移学习，平均Dice值增加到0.8835。而整个MRI体积所需的分割时间为14.8秒。在MICCAI数据集中，通过从较大的EADC-ADNI HarP数据集中进行转移学习，平均Dice值增加到0.8835。而整个MRI体积所需的分割时间为14.8秒。在MICCAI数据集中，通过从较大的EADC-ADNI HarP数据集中进行转移学习，平均Dice值增加到0.8835。