Carass Aaron, Shao Muhan, Li Xiang, Dewey Blake E, Blitz Ari M, Roy Snehashis, Pham Dzung L, Prince Jerry L, Ellingsen Lotta M
Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.
Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA.
Patch Based Tech Med Imaging (2017). 2017 Sep;10530:20-28. doi: 10.1007/978-3-319-67434-6_3. Epub 2017 Aug 31.
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.
许多脑部疾病都与脑室扩大有关;正常压力脑积水(NPH)就是一个例子。NPH表现出类似痴呆的症状,由于其慢性病程和非特异性症状,常被误诊为阿尔茨海默病。然而,与其他形式的痴呆不同,NPH可以通过手术治疗,对适当选择的患者成功率超过80%。诊断该疾病需要准确评估脑室,特别是其各个子区域。现有的分割算法无法准确识别患有这种极端病理状况患者的脑室。我们提出了一种全脑分割方法的改进,该方法能准确识别脑室并将其分割为四个子区域。我们的工作是基于补丁的组织分割和基于多图谱配准的标记相结合。我们对NPH患者进行了验证,证明相对于现有最先进方法具有卓越性能。