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Segmentation of Intracranial Vessels and Aneurysms in Phase Contrast Magnetic Resonance Angiography Using Multirange Filters and Local Variances

机译:使用多范围滤镜和局部方差在相衬磁共振血管造影中颅内血管和动脉瘤的分割

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摘要

Segmentation of intensity varying and low-contrast structures is an extremely challenging and rewarding task. In computer-aided diagnosis of intracranial aneurysms, segmenting the high-intensity major vessels along with the attached low-contrast aneurysms is essential to the recognition of this lethal vascular disease. It is particularly helpful in performing early and noninvasive diagnosis of intracranial aneurysms using phase contrast magnetic resonance angiographic (PC-MRA) images. The major challenges of developing a PC-MRA-based segmentation method are the significantly varying voxel intensity inside vessels with different flow velocities and the signal loss in the aneurysmal regions where turbulent flows occur. This paper proposes a novel intensity-based algorithm to segment intracranial vessels and the attached aneurysms. The proposed method can handle intensity varying vasculatures and also the low-contrast aneurysmal regions affected by turbulent flows. It is grounded on the use of multirange filters and local variances to extract intensity-based image features for identifying contrast varying vasculatures. The extremely low-intensity region affected by turbulent flows is detected according to the topology of the structure detected by multirange filters and local variances. The proposed method is evaluated using a phantom image volume with an aneurysm and four clinical cases. It achieves 0.80 dice score in the phantom case. In addition, different components of the proposed method—the multirange filters, local variances, and topology-based detection—are evaluated in the comparison between the proposed method and its lower complexity variants. Owing to the analogy between these variants and existing vascular segmentation methods, this comparison also exemplifies the advantage of the proposed method over the existing approaches. It analyzes the weaknesses of these existing approaches and justifies the use of every component involved in the pro- osed method. It is shown that the proposed method is capable of segmenting blood vessels and the attached aneurysms on PC-MRA images.
机译:分割强度变化和低对比度的结构是一项极富挑战性和回报的任务。在颅内动脉瘤的计算机辅助诊断中,将高强度大血管与所附的低对比度动脉瘤进行分割对于识别这种致命的血管疾病至关重要。使用相衬磁共振血管造影(PC-MRA)图像对颅内动脉瘤进行早期和非侵入性诊断特别有用。开发基于PC-MRA的分割方法的主要挑战是血管内部具有不同流速的体素强度显着变化,并且在发生湍流的动脉瘤区域中信号丢失。本文提出了一种新的基于强度的分割颅内血管和附属动脉瘤的算法。所提出的方法可以处理强度变化的脉管系统以及受湍流影响的低对比度的动脉瘤区域。它基于使用多范围滤镜和局部方差来提取基于强度的图像特征,以识别对比度变化的脉管系统。根据多范围滤波器检测到的结构的拓扑和局部变化,检测受湍流影响的极低强度区域。使用带有动脉瘤的幻影图像体积和四个临床病例对提出的方法进行评估。在幻影情况下,它的骰子得分为0.80。另外,在所提出的方法与其较低复杂度变量之间的比较中,对所提出方法的不同组成部分(多范围滤波器,局部方差和基于拓扑的检测)进行了评估。由于这些变体与现有的血管分割方法之间的类比,该比较还例证了所提出的方法相对于现有方法的优势。它分析了这些现有方法的缺点,并证明了所建议方法所涉及的每个组件的使用都是合理的。结果表明,所提出的方法能够在PC-MRA图像上分割血管和附着的动脉瘤。

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