Ukwatta Eranga, Yuan Jing, Rajchl Martin, Fenster Aaron
Robarts Research Institute, The University of Western Ontario, London, ON, Canada.
Med Image Comput Comput Assist Interv. 2012;15(Pt 3):377-84. doi: 10.1007/978-3-642-33454-2_47.
Magnetic resonance (MR) imaging of carotid atherosclerosis biomarkers are increasingly being investigated for the risk assessment of vulnerable plaques. A fast and robust 3D segmentation of the carotid adventitia (AB) and lumen-intima (LIB) boundaries can greatly alleviate the measurement burden of generating quantitative imaging biomarkers in clinical research. In this paper, we propose a novel global optimization-based approach to segment the carotid AB and LIB from 3D T1-weighted black blood MR images, by simultaneously evolving two coupled surfaces with enforcement of anatomical consistency of the AB and LIB. We show that the evolution of two surfaces at each discrete time-frame can be optimized exactly and globally by means of convex relaxation. Our continuous max-flow based algorithm is implemented in GPUs to achieve high computational performance. The experiment results from 16 carotid MR images show that the algorithm obtained high agreement with manual segmentations and achieved high repeatability in segmentation.
用于评估易损斑块风险的颈动脉粥样硬化生物标志物的磁共振（MR）成像研究日益增多。快速且稳健地对颈动脉外膜（AB）和管腔内膜（LIB）边界进行三维分割，可极大减轻临床研究中生成定量成像生物标志物的测量负担。在本文中，我们提出了一种基于全局优化的新方法，通过同时演化两个耦合曲面并确保AB和LIB的解剖学一致性，从三维T1加权黑血MR图像中分割出颈动脉AB和LIB。我们表明，在每个离散时间帧处，两个曲面的演化可通过凸松弛精确且全局地进行优化。我们基于连续最大流的算法在图形处理器（GPU）上实现，以实现高计算性能。来自16幅颈动脉MR图像的实验结果表明，该算法与手动分割高度一致，且分割具有高重复性。
