Lo Kwee-Seong Medical Image Analysis Laboratory, Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong.
IEEE Trans Image Process. 2013 Mar;22(3):845-59. doi: 10.1109/TIP.2012.2216274. Epub 2012 Aug 30.
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 proposed method. It is shown that the proposed method is capable of segmenting blood vessels and the attached aneurysms on PC-MRA images.
强度变化和低对比度结构的分割是一项极具挑战性和有价值的任务。在颅内动脉瘤的计算机辅助诊断中，分割高强度的主要血管以及附着的低对比度动脉瘤对于识别这种致命的血管疾病至关重要。它在使用相位对比磁共振血管造影 (PC-MRA) 图像进行颅内动脉瘤的早期和非侵入性诊断方面特别有帮助。基于 PC-MRA 的分割方法开发的主要挑战是不同流速的血管内的体素强度变化显著，以及在发生湍流的动脉瘤区域的信号丢失。本文提出了一种新的基于强度的算法来分割颅内血管和附着的动脉瘤。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动脉瘤区域。它基于使用多范围滤波器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。根据多范围滤波器和局部方差检测到的结构的拓扑结构，检测受湍流影响的极低强度区域。该方法使用带有动脉瘤的体模图像体积和四个临床病例进行评估。在体模病例中，它实现了 0.80 的骰子得分。此外，在所提出的方法及其复杂度较低的变体之间的比较中，评估了该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。由于这些变体与现有血管分割方法之间的相似性，这种比较还例示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弱点，并证明了所提出的方法中涉及的每个组件的使用是合理的。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。
