多队列不完全海马翻转的自动评级
Automatic rating of incomplete hippocampal inversions evaluated across multiple cohorts
AI总结：
本文首次提出自动评级不完全海马翻转（IHI），通过预测四个解剖标准并相加得到可解释评分，在多个大规模队列上验证了深度学习模型（如conv5-FC3）优于岭回归，且多队列训练提升泛化能力。
摘要：
不完全海马翻转（Incomplete Hippocampal Inversion, IHI），有时也称为海马旋转不良，是一种非典型的海马解剖模式，在普通人群中约有20%的个体存在。IHI可以在T1加权MRI图像的冠状切片上进行视觉评估，使用结合四个解剖标准的综合评分。IHI与多种脑部疾病（如癫痫、精神分裂症）有关。然而，这些研究基于小样本。此外，导致IHI发生的因素（遗传或环境）在很大程度上尚不清楚。因此，需要大规模研究来进一步理解IHI及其与神经和精神疾病的潜在关联。然而，视觉评估耗时且乏味，因此有必要开发自动化方法。在本文中，我们首次提出自动对IHI进行评级。我们通过预测四个解剖标准，然后将它们相加形成IHI评分，从而提供可解释评分的优势。我们对不同的机器学习方法和训练策略进行了广泛的实验研究。我们使用了多种深度学习模型（conv5-FC3、ResNet和SECNN）以及岭回归进行自动评级。我们使用不同队列研究了模型的泛化能力，并进行了多队列学习。我们依赖于来自IMAGEN研究的2,008名参与者、QTIM/QTAB研究的993名和403名参与者以及UKBiobank的985名受试者的大规模人群。我们表明，深度学习模型优于岭回归。我们证明了conv5-FC3网络的性能至少与更复杂的网络相当，同时保持低复杂性和计算时间。我们表明，在单一队列上训练可能缺乏变异性，而在多个队列上训练则提高了泛化能力。
英文摘要：
Incomplete Hippocampal Inversion (IHI), sometimes called hippocampal malrotation, is an atypical anatomical pattern of the hippocampus found in about 20% of the general population. IHI can be visually assessed on coronal slices of T1 weighted MR images, using a composite score that combines four anatomical criteria. IHI has been associated with several brain disorders (epilepsy, schizophrenia). However, these studies were based on small samples. Furthermore, the factors (genetic or environmental) that contribute to the genesis of IHI are largely unknown. Large-scale studies are thus needed to further understand IHI and their potential relationships to neurological and psychiatric disorders. However, visual evaluation is long and tedious, justifying the need for an automatic method. In this paper, we propose, for the first time, to automatically rate IHI. We proceed by predicting four anatomical criteria, which are then summed up to form the IHI score, providing the advantage of an interpretable score. We provided an extensive experimental investigation of different machine learning methods and training strategies. We performed automatic rating using a variety of deep learning models (conv5-FC3, ResNet and SECNN) as well as a ridge regression. We studied the generalization of our models using different cohorts and performed multi-cohort learning. We relied on a large population of 2,008 participants from the IMAGEN study, 993 and 403 participants from the QTIM/QTAB studies as well as 985 subjects from the UKBiobank. We showed that deep learning models outperformed a ridge regression. We demonstrated that the performances of the conv5-FC3 network were at least as good as more complex networks while maintaining a low complexity and computation time. We showed that training on a single cohort may lack in variability while training on several cohorts improves generalization.
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