Poiret Clement, Bouyeure Antoine, Patil Sandesh, Grigis Antoine, Duchesnay Edouard, Faillot Matthieu, Bottlaender Michel, Lemaitre Frederic, Noulhiane Marion
UNIACT, NeuroSpin, CEA Paris-Saclay, Frederic Joliot Institute, Gif-sur-Yvette, France.
NeuroSpin, CEA Paris-Saclay, Frederic Joliot Institute, Gif-sur-Yvette, France.
Front Neuroinform. 2023 Jun 15;17:1130845. doi: 10.3389/fninf.2023.1130845. eCollection 2023.
The hippocampal subfields, pivotal to episodic memory, are distinct both in terms of cyto- and myeloarchitectony. Studying the structure of hippocampal subfields is crucial to understand volumetric trajectories across the lifespan, from the emergence of episodic memory during early childhood to memory impairments found in older adults. However, segmenting hippocampal subfields on conventional MRI sequences is challenging because of their small size. Furthermore, there is to date no unified segmentation protocol for the hippocampal subfields, which limits comparisons between studies. Therefore, we introduced a novel segmentation tool called HSF short for hippocampal segmentation factory, which leverages an end-to-end deep learning pipeline. First, we validated HSF against currently used tools (ASHS, HIPS, and HippUnfold). Then, we used HSF on 3,750 subjects from the HCP development, young adults, and aging datasets to study the effect of age and sex on hippocampal subfields volumes. Firstly, we showed HSF to be closer to manual segmentation than other currently used tools ( < 0.001), regarding the Dice Coefficient, Hausdorff Distance, and Volumetric Similarity. Then, we showed differential maturation and aging across subfields, with the dentate gyrus being the most affected by age. We also found faster growth and decay in men than in women for most hippocampal subfields. Thus, while we introduced a new, fast and robust end-to-end segmentation tool, our neuroanatomical results concerning the lifespan trajectories of the hippocampal subfields reconcile previous conflicting results.
海马体亚区对于情景记忆至关重要，在细胞结构和髓鞘结构方面都各不相同。研究海马体亚区的结构对于理解整个生命周期中的体积变化轨迹至关重要，从幼儿期情景记忆的出现到老年人出现的记忆障碍。然而，在传统MRI序列上分割海马体亚区具有挑战性，因为它们尺寸较小。此外，迄今为止，尚无针对海马体亚区的统一分割方案，这限制了研究之间的比较。因此，我们引入了一种名为HSF（海马体分割工厂的缩写）的新型分割工具，它利用了端到端的深度学习管道。首先，我们将HSF与当前使用的工具（ASHS、HIPS和HippUnfold）进行了验证。然后，我们对来自HCP发育、年轻成年人和衰老数据集的3750名受试者使用了HSF，以研究年龄和性别对海马体亚区体积的影响。首先，在骰子系数、豪斯多夫距离和体积相似度方面，我们表明HSF比其他当前使用的工具更接近手动分割（<0.001）。然后，我们展示了各亚区不同的成熟和衰老情况，齿状回受年龄影响最大。我们还发现，大多数海马体亚区男性的生长和衰退速度比女性更快。因此，虽然我们引入了一种新的、快速且强大的端到端分割工具，但我们关于海马体亚区生命周期轨迹的神经解剖学结果调和了先前相互矛盾的结果。
