Segmentation of Ulcerated Plaque: Evaluation and Optimization of a Semi-automatic Method for Tracking the Progression of Carotid Atherosclerosis

机译：溃疡斑块的分割：用于跟踪颈动脉粥样硬化进展的半自动方法的评价和优化

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A semi-automatic method for segmenting carotid lumen and plaque from three-dimensional vascular ultrasound (US) images has been developed. We examine its ability to distinguish changes in carotid vessel and plaque surface morphology, such as those caused by plaque ulceration. Two stenosed vessel phantoms were imaged using a 3D US imaging system. The phantoms were identical except for the inclusion of a hemispherical cut in the side of one of the vessels, in order to simulate the development of an ulceration. Ultrasound images of the phantoms were segmented using our algorithm, then the resulting surfaces were registered to one another using a rigid-body iterative closest point (ICP) algorithm. The volume of ulceration was determined by finding the difference between the two segmented surfaces in a region of interest surrounding the ulceration. Since the true volume of the ulceration was known a priori, an optimization strategy was used to tune the deformable model to better segment the ulceration. Analysis of ulceration volume as a function of the deformable model's parameters show that 1) large ulcerations are easily identified in our test case, and 2) the model is well behaved with respect to its parameters, suggesting that an automatic strategy for volumetric optimization is feasible.

机译：已经开发了一种半自动方法，用于分割颈动脉腔和三维血管超声（US）图像的斑块。我们检查其区分颈动脉血管和斑块表面形态的变化的能力，例如由斑块溃疡引起的那些。使用3D US成像系统对两个狭窄的血管映像进行成像。除了包含其中一个血管侧的半球形切口之外，幽灵是相同的，以模拟溃疡的发展。使用我们的算法分割幽灵的超声图像，然后使用刚体迭代最近点（ICP）算法彼此登记所得表面。通过在围绕溃疡周围的感兴趣区域中的两个分段表面之间的差异来确定溃疡的体积。由于ulcered的真实量已知先验，因此使用优化策略来调整可变形模型以更好地段才能进行溃疡。作为可变形模型参数的函数的溃疡卷分析显示，1）在我们的测试用例中容易识别出大的溃疡，并且2）模型对其参数进行了很好的表现，表明体积优化的自动策略是可行的。

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《Society of Photo-Optical Instrumentation Engineers Conference on Medical Imaging : Image Processing》|2001年||共10页
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Jeremy D. Gill; Hanif M. Ladak; Aaron Fenster;
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中图分类 N-532;
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3-D ultrasonography; image segmentation; deformable models; atherosclerosis;

机译：3-D超声检查;图像分割;可变形模型;动脉粥样硬化;

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专利

1. A Semi-Automatic Segmentation Method for the Structural Analysis of Carotid Atherosclerotic Plaques by Computed Tomography Angiography [J] . Florentino Luciano Caetano dos Santos, Atte Joutsen, Mitsugu Terada, Journal of atherosclerosis and thrombosis. . 2014,第9期

机译：半自动分割方法的计算机断层扫描血管造影术分析颈动脉粥样硬化斑块。
2. Three-Dimensional Carotid Plaque Progression Simulation Using Meshless Generalized Finite Difference Method Based on Multi-Year MRI Patient-Tracking Data [J] . Chun Yang, Dalin Tang, Satya Atluri Computer Modeling in Engineering & Sciences . 2010,第1期

机译：基于多年MRI患者跟踪数据的无网格广义有限差分法三维颈动脉斑块进展模拟
3. Meshless Generalized Finite Difference Method and Human Carotid Atherosclerotic Plaque Progression Simulation Using Multi-Year MRI Patient-Tracking Data [J] . Chun Yang, Dalin Tang, Chun Yuan, Computer Modeling in Engineering & Sciences . 2008,第2期

机译：基于多年MRI患者跟踪数据的无网格广义有限差分法和人颈动脉粥样硬化斑块进展模拟
4. Segmentation of Ulcerated Plaque: Evaluation and Optimization of a Semi-automatic Method for Tracking the Progression of Carotid Atherosclerosis [C] . Jeremy D. Gill, Hanif M. Ladak, Aaron Fenster Society of Photo-Optical Instrumentation Engineers Conference on Medical Imaging : Image Processing . 2001

机译：溃疡斑块的分割：用于跟踪颈动脉粥样硬化进展的半自动方法的评价和优化
5. Carotid plaque specimens: Semi-automatic orientation correction of micro-MR and micro-CT images driven by axial feature segmentation [D] . Nasui, Otilia Cristina. 2015

机译：颈动脉斑块标本：半自动取向校正通过轴向特征分割驱动的微mR和微型CT图像
6. Three-Dimensional Carotid Plaque Progression Simulation Using Meshless Generalized Finite Difference Method Based on Multi-Year MRI Patient-Tracking Data [O] . Chun Yang, Dalin Tang, Satya Atluri -1

机译：基于多年MRI患者跟踪数据的无网义普遍有限差分法的三维颈动脉凝血探测
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Segmentation of Ulcerated Plaque: Evaluation and Optimization of a Semi-automatic Method for Tracking the Progression of Carotid Atherosclerosis

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