Estrada Santiago, Kügler David, Bahrami Emad, Xu Peng, Mousa Dilshad, Breteler Monique M B, Aziz N Ahmad, Reuter Martin
AI in Medical Imaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.
Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.
Imaging Neurosci (Camb). 2023 Nov 21;1:1-32. doi: 10.1162/imag_a_00034. eCollection 2023 Nov 1.
The hypothalamus plays a crucial role in the regulation of a broad range of physiological, behavioral, and cognitive functions. However, despite its importance, only a few small-scale neuroimaging studies have investigated its substructures, likely due to the lack of fully automated segmentation tools to address scalability and reproducibility issues of manual segmentation. While the only previous attempt to automatically sub-segment the hypothalamus with a neural network showed promise for 1.0 mm isotropic T1-weighted (T1w) magnetic resonance imaging (MRI), there is a need for an automated tool to sub-segment also high-resolutional (HiRes) MR scans, as they are becoming widely available, and include structural detail also from multi-modal MRI. We, therefore, introduce a novel, fast, and fully automated deep-learning method named for sub-segmentation of the hypothalamus and adjacent structures on 0.8 mm isotropic T1w and T2w brain MR images that is robust to missing modalities. We extensively validate our model with respect to segmentation accuracy, generalizability, in-session test-retest reliability, and sensitivity to replicate hypothalamic volume effects (e.g., sex differences). The proposed method exhibits high segmentation performance both for standalone T1w images as well as for T1w/T2w image pairs. Even with the additional capability to accept flexible inputs, our model matches or exceeds the performance of state-of-the-art methods with fixed inputs. We, further, demonstrate the generalizability of our method in experiments with 1.0 mm MR scans from both the Rhineland Study and the UK Biobank-an independent dataset never encountered during training with different acquisition parameters and demographics. Finally, can perform the segmentation in less than a minute (graphical processing unit [GPU]) and will be available in the open source neuroimaging software suite, offering a validated, efficient, and scalable solution for evaluating imaging-derived phenotypes of the hypothalamus.
下丘脑在广泛的生理、行为和认知功能调节中起着关键作用。然而，尽管其很重要，但仅有少数小规模神经影像学研究对其次级结构进行了调查，这可能是由于缺乏能解决手动分割的可扩展性和可重复性问题的全自动分割工具。虽然之前唯一一次尝试使用神经网络自动对下丘脑进行子分割在1.0毫米各向同性T1加权（T1w）磁共振成像（MRI）中显示出了前景，但仍需要一种自动工具来对子分割高分辨率（HiRes）MR扫描，因为它们正变得广泛可用，并且还包含来自多模态MRI的结构细节。因此，我们引入了一种新颖、快速且完全自动化的深度学习方法，该方法能够在0.8毫米各向同性T1w和T2w脑MR图像上对下丘脑及相邻结构进行子分割，并且对缺失模态具有鲁棒性。我们在分割准确性、通用性、会话内重测可靠性以及对复制下丘脑体积效应（例如性别差异）的敏感性方面对我们的模型进行了广泛验证。所提出的方法在单独的T1w图像以及T1w/T2w图像对上均表现出高分割性能。即使具有接受灵活输入的额外能力，我们的模型在固定输入的情况下仍能匹配或超过现有最先进方法的性能。此外，我们在来自莱茵兰研究和英国生物银行的1.0毫米MR扫描实验中证明了我们方法的通用性，这是一个在训练期间从未遇到过的具有不同采集参数和人口统计学特征的独立数据集。最后，该方法能够在不到一分钟的时间内（使用图形处理单元[GPU]）完成分割，并且将在开源神经影像学软件套件中可用，为评估下丘脑的影像学衍生表型提供了一个经过验证、高效且可扩展的解决方案。
