Despite the crucial role of the hypothalamus in the regulation of the human body, neuroimaging studies of this structure and its nuclei are scarce. Such scarcity partially stems from the lack of automated segmentation tools, since manual delineation suffers from scalability and reproducibility issues. Due to the small size of the hypothalamus and the lack of image contrast in its vicinity, automated segmentation is difficult and has been long neglected by widespread neuroimaging packages like FreeSurfer or FSL. Nonetheless, recent advances in deep machine learning are enabling us to tackle difficult segmentation problems with high accuracy. In this paper we present a fully automated tool based on a deep convolutional neural network, for the segmentation of the whole hypothalamus and its subregions from T1-weighted MRI scans. We use aggressive data augmentation in order to make the model robust to T1-weighted MR scans from a wide array of different sources, without any need for preprocessing. We rigorously assess the performance of the presented tool through extensive analyses, including: inter- and intra-rater variability experiments between human observers; comparison of our tool with manual segmentation; comparison with a an automated method based on multi-atlas segmentation; assessment of robustness by quality control analysis of a larger, heterogeneous dataset (ADNI); and indirect evaluation with a volumetric study performed on ADNI. The presented model outperforms multi-atlas segmentation scores as well as inter-rater accuracy level, and approaches intra-rater precision. Our method does not require any preprocessing and runs in less than a second on a GPU, and approximately 10 s on a CPU. The source code as well as the trained model are publicly available at https://github.com/BBillot/hypothalamus_seg.

中文翻译：

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脑 MRI 中下丘脑和相关亚单位的自动分割

尽管下丘脑在人体调节中起着至关重要的作用，但对该结构及其细胞核的神经影像学研究很少。这种稀缺性部分源于缺乏自动分割工具，因为手动描绘存在可扩展性和可重复性问题。由于下丘脑尺寸小且其附近缺乏图像对比度，自动分割很困难，并且长期以来一直被 FreeSurfer 或 FSL 等广泛使用的神经成像软件包所忽视。尽管如此，深度机器学习的最新进展使我们能够以高精度解决困难的分割问题。在本文中，我们提出了一种基于深度卷积神经网络的全自动工具，用于从 T1 加权 MRI 扫描中分割整个下丘脑及其子区域。我们使用积极的数据增强来使模型对来自各种不同来源的 T1 加权 MR 扫描具有鲁棒性，而无需任何预处理。我们通过广泛的分析严格评估所提供工具的性能，包括：人类观察者之间的评价者间和评价者内变异性实验；我们的工具与手动分割的比较；与基于多图谱分割的自动化方法的比较；通过对更大的异构数据集 (ADNI) 进行质量控制分析来评估稳健性；并通过对 ADNI 进行的体积研究进行间接评估。所提出的模型优于多图谱分割分数以及评分者间的准确度水平，并接近评分者内的精确度。我们的方法不需要任何预处理，并且在 GPU 上运行不到一秒，在 CPU 上大约需要 10 秒。源代码以及经过训练的模型可在 https://github.com/BBillot/hypothalamus_seg 上公开获得。