全脑包缩与病理:脑室肿大患者的验证。

Aaron Carass, Muhan Shao, Xiang Li, Blake E Dewey, Ari M Blitz, Snehashis Roy, Dzung L Pham, Jerry L Prince, Lotta M Ellingsen
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引用次数: 11

摘要

许多脑部疾病与脑室肿大有关;常压性脑积水(NPH)就是一个例子。NPH表现为痴呆样症状,由于其慢性性质和非特异性表现症状,常被误诊为阿尔茨海默病。然而,与其他形式的痴呆症不同,NPH可以通过手术治疗,在适当选择的患者中成功率超过80%。准确评估心室,特别是其亚室,是诊断病情所必需的。现有的分割算法无法准确识别这种极端病理患者的心室。我们提出了一种改进的全脑分割方法,准确地识别心室,并将它们分割成四个子室。我们的工作是基于补丁的组织分割和基于多图谱注册的标记的结合。我们包括对NPH患者的验证,展示了与最先进的方法相比的优越性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Whole Brain Parcellation with Pathology: Validation on Ventriculomegaly Patients.

Numerous brain disorders are associated with ventriculomegaly; normal pressure hydrocephalus (NPH) is one example. NPH presents with dementia-like symptoms and is often misdiagnosed as Alzheimer's due to its chronic nature and nonspecific presenting symptoms. However, unlike other forms of dementia NPH can be treated surgically with an over 80% success rate on appropriately selected patients. Accurate assessment of the ventricles, in particular its sub-compartments, is required to diagnose the condition. Existing segmentation algorithms fail to accurately identify the ventricles in patients with such extreme pathology. We present an improvement to a whole brain segmentation approach that accurately identifies the ventricles and parcellates them into four sub-compartments. Our work is a combination of patch-based tissue segmentation and multi-atlas registration-based labeling. We include a validation on NPH patients, demonstrating superior performance against state-of-the-art methods.

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Brain Image Labeling Using Multi-atlas Guided 3D Fully Convolutional Networks. Learning-Based Estimation of Functional Correlation Tensors in White Matter for Early Diagnosis of Mild Cognitive Impairment. Whole Brain Parcellation with Pathology: Validation on Ventriculomegaly Patients. Multiple Sclerosis Lesion Segmentation Using Joint Label Fusion. 4D Multi-atlas Label Fusion using Longitudinal Images.
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