Ellingsen Lotta M, Roy Snehashis, Carass Aaron, Blitz Ari M, Pham Dzung L, Prince Jerry L
Department of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland; Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.
CNRM, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892, USA.
Proc SPIE Int Soc Opt Eng. 2016 Mar 21;9784. doi: 10.1117/12.2216511. Epub 2016 Feb 27.
Normal pressure hydrocephalus (NPH) affects older adults and is thought to be caused by obstruction of the normal flow of cerebrospinal fluid (CSF). NPH typically presents with cognitive impairment, gait dysfunction, and urinary incontinence, and may account for more than five percent of all cases of dementia. Unlike most other causes of dementia, NPH can potentially be treated and the neurological dysfunction reversed by shunt surgery or endoscopic third ventriculostomy (ETV), which drain excess CSF. However, a major diagnostic challenge remains to robustly identify shunt-responsive NPH patients from patients with enlarged ventricles due to other neurodegenerative diseases. Currently, radiologists grade the severity of NPH by detailed examination and measurement of the ventricles based on stacks of 2D magnetic resonance images (MRIs). Here we propose a new method to automatically segment and label different compartments of the ventricles in NPH patients from MRIs. While this task has been achieved in healthy subjects, the ventricles in NPH are both enlarged and deformed, causing current algorithms to fail. Here we combine a patch-based tissue classification method with a registration-based multi-atlas labeling method to generate a novel algorithm that labels the lateral, third, and fourth ventricles in subjects with ventriculomegaly. The method is also applicable to other neurodegenerative diseases such as Alzheimer's disease; a condition considered in the differential diagnosis of NPH. Comparison with state of the art segmentation techniques demonstrate substantial improvements in labeling the enlarged ventricles, indicating that this strategy may be a viable option for the diagnosis and characterization of NPH.
正常压力脑积水（NPH）影响老年人，被认为是由脑脊液（CSF）正常流动受阻引起的。NPH通常表现为认知障碍、步态功能障碍和尿失禁，可能占所有痴呆病例的5%以上。与大多数其他痴呆病因不同，NPH有可能通过分流手术或内镜下第三脑室造瘘术（ETV）进行治疗，使神经功能障碍得到逆转，这两种手术可引流多余的脑脊液。然而，一个主要的诊断挑战仍然是如何从因其他神经退行性疾病导致脑室扩大的患者中准确识别出对分流手术有反应的NPH患者。目前，放射科医生通过基于二维磁共振图像（MRI）堆栈对脑室进行详细检查和测量来对NPH的严重程度进行分级。在此，我们提出一种新方法，可根据MRI自动分割和标记NPH患者脑室的不同腔室。虽然这项任务在健康受试者中已经完成，但NPH患者的脑室既扩大又变形，导致当前算法失效。在此，我们将基于补丁的组织分类方法与基于配准的多图谱标记方法相结合，生成一种新算法，用于标记脑室扩大患者的侧脑室、第三脑室和第四脑室。该方法也适用于其他神经退行性疾病，如阿尔茨海默病；这是一种在NPH鉴别诊断中需要考虑的疾病。与现有最先进的分割技术相比，在标记扩大脑室方面有显著改进，表明该策略可能是NPH诊断和特征描述的可行选择。
