Segmentation of Leukoaraiosis on Noncontrast Head CT Using CT-MRI Paired Data Without Human Annotation

IF 2.6 3区 心理学 Q2 BEHAVIORAL SCIENCES
Wi-Sun Ryu, Jae W. Song, Jae-Sung Lim, Ju Hyung Lee, Leonard Sunwoo, Dongmin Kim, Dong-Eog Kim, Hee-Joon Bae, Myungjae Lee, Beom Joon Kim
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引用次数: 0

Abstract

Objective

Evaluating leukoaraiosis (LA) on CT is challenging due to its low contrast and similarity to parenchymal gliosis. We developed and validated a deep learning algorithm for LA segmentation using CT-MRIFLAIR paired data from a multicenter Korean registry and tested it in a US dataset.

Methods

We constructed a large multicenter dataset of CT–FLAIR MRI pairs. Using validated software to segment white matter hyperintensity (WMH) on FLAIR, we generated pseudo-ground-truth LA labels on CT through deformable image registration. A 2D nnU-Net architecture was trained solely on CT images and registered masks. Performance was evaluated using the Dice similarity coefficient (DSC), concordance correlation coefficient (CCC), and Pearson correlation across internal, external, and US validation cohorts. Clinical associations of predicted LA volume with age, risk factors, and poststroke outcomes were also analyzed.

Results

The external test set yielded a DSC of 0.527, with high volume correlations against registered LA (r = 0.953) and WMH (r = 0.951). In the external testing and US datasets, predicted LA volumes correlated with Fazekas grade (r = 0.832–0.891) and the correlations were consistent across CT vendors and infarct volumes. In an independent clinical cohort (n = 867), LA volume was independently associated with age, vascular risk factors, and 3-month functional outcomes.

Interpretation

Our deep learning algorithm offers a reproducible method for LA segmentation on CT, bridging the gap between CT and MRI assessments in patients with ischemic stroke.

Abstract Image

利用CT- mri配对数据在非对比头部CT上分割白质病变
目的对白质病变(LA)的CT评价具有挑战性,因为它与实质胶质瘤的对比低且相似。我们开发并验证了一种用于洛杉矶分割的深度学习算法,该算法使用来自多中心韩国注册表的ct - mri配对数据,并在美国数据集中进行了测试。方法构建了一个大型的CT-FLAIR MRI对多中心数据集。使用经过验证的软件在FLAIR上分割白质高强度(WMH),我们通过可变形图像配准在CT上生成伪地真LA标签。2D nnU-Net架构仅在CT图像和注册掩码上进行训练。使用Dice相似系数(DSC)、一致性相关系数(CCC)和Pearson相关性在内部、外部和美国验证队列中进行评估。预测LA容量与年龄、危险因素和脑卒中后结局的临床关联也进行了分析。结果外部测试集的DSC为0.527,与注册的LA (r = 0.953)和WMH (r = 0.951)有很高的体积相关性。在外部测试和美国数据集中,预测的LA体积与Fazekas分级相关(r = 0.832-0.891),并且在CT供应商和梗死体积之间的相关性是一致的。在一个独立的临床队列(n = 867)中,LA容积与年龄、血管危险因素和3个月功能结局独立相关。我们的深度学习算法为CT上的LA分割提供了一种可重复的方法,弥合了缺血性卒中患者CT和MRI评估之间的差距。
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来源期刊
Brain and Behavior
Brain and Behavior BEHAVIORAL SCIENCES-NEUROSCIENCES
CiteScore
5.30
自引率
0.00%
发文量
352
审稿时长
14 weeks
期刊介绍: Brain and Behavior is supported by other journals published by Wiley, including a number of society-owned journals. The journals listed below support Brain and Behavior and participate in the Manuscript Transfer Program by referring articles of suitable quality and offering authors the option to have their paper, with any peer review reports, automatically transferred to Brain and Behavior. * [Acta Psychiatrica Scandinavica](https://publons.com/journal/1366/acta-psychiatrica-scandinavica) * [Addiction Biology](https://publons.com/journal/1523/addiction-biology) * [Aggressive Behavior](https://publons.com/journal/3611/aggressive-behavior) * [Brain Pathology](https://publons.com/journal/1787/brain-pathology) * [Child: Care, Health and Development](https://publons.com/journal/6111/child-care-health-and-development) * [Criminal Behaviour and Mental Health](https://publons.com/journal/3839/criminal-behaviour-and-mental-health) * [Depression and Anxiety](https://publons.com/journal/1528/depression-and-anxiety) * Developmental Neurobiology * [Developmental Science](https://publons.com/journal/1069/developmental-science) * [European Journal of Neuroscience](https://publons.com/journal/1441/european-journal-of-neuroscience) * [Genes, Brain and Behavior](https://publons.com/journal/1635/genes-brain-and-behavior) * [GLIA](https://publons.com/journal/1287/glia) * [Hippocampus](https://publons.com/journal/1056/hippocampus) * [Human Brain Mapping](https://publons.com/journal/500/human-brain-mapping) * [Journal for the Theory of Social Behaviour](https://publons.com/journal/7330/journal-for-the-theory-of-social-behaviour) * [Journal of Comparative Neurology](https://publons.com/journal/1306/journal-of-comparative-neurology) * [Journal of Neuroimaging](https://publons.com/journal/6379/journal-of-neuroimaging) * [Journal of Neuroscience Research](https://publons.com/journal/2778/journal-of-neuroscience-research) * [Journal of Organizational Behavior](https://publons.com/journal/1123/journal-of-organizational-behavior) * [Journal of the Peripheral Nervous System](https://publons.com/journal/3929/journal-of-the-peripheral-nervous-system) * [Muscle & Nerve](https://publons.com/journal/4448/muscle-and-nerve) * [Neural Pathology and Applied Neurobiology](https://publons.com/journal/2401/neuropathology-and-applied-neurobiology)
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