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首页> 外文会议>Image Processing pt.1 >Atlas-based method for segmentation of cerebral vascular trees from phase-contrast magnetic resonance angiography
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Atlas-based method for segmentation of cerebral vascular trees from phase-contrast magnetic resonance angiography

机译：基于Atlas的相衬磁共振血管成像分割脑血管树的方法

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Phase-contrast magnetic resonance angiography (PC-MRA) can produce phase images which are 3-dimensional pictures of vascular structures. However, it also provides magnitude images, containing anatomical - but no vascular - data. Classically, algorithms dedicated to PC-MRA segmentation detect the cerebral vascular tree by only working on phase images. We propose here a new approach for segmentation of cerebral blood vessels in PC-MRA using both types of images. This approach is based on the hypothesis that a magnitude image contains anatomical information useful for vascular structures detection. That information can then be transposed from a normal case to any patient image by image registration. An atlas of the whole head has been developed in order to store such anatomical knowledge. It divides a magnitude image into several 'vascular areas', each one having specific vessel properties. The atlas can be applied on any magnitude image of an entire or nearly entire head by deformable matching, thus helping to segment blood vessels from the associated phase image. The segmentation method used afterwards is composed of a topology-conserving region growing algorithm using adaptative threshold values depending on the current region of the atlas. This algorithm builds the arterial and venous trees by iteratively adding voxels which are selected according to their greyscale value and the variation of values in their neighborhood. The topology conservation is guaranteed by only selecting simple points during the growing process. The method has been performed on 15 PC-MRA's of the brain. The results have been validated using MIP and 3D surface rendering visualization; a comparison to other results obtained without an atlas proves that atlas-based methods are an effective way to optimize vascular segmentation strategies.
机译：相衬磁共振血管造影（PC-MRA）可以产生相图像，该图像是血管结构的3维图像。但是，它也提供幅值图像，其中包含解剖数据，但不包含血管数据。传统上，专用于PC-MRA分割的算法仅通过处理相位图像来检测脑血管树。我们在这里提出一种使用两种图像对PC-MRA中的脑血管进行分割的新方法。这种方法基于这样的假设，即幅值图像包含可用于检测血管结构的解剖信息。然后可以通过图像配准将该信息从正常情况转换为任何患者图像。为了存储这种解剖学知识，已经开发了整个头部的图集。它将幅值图像分为几个“血管区域”，每个区域都有特定的血管属性。该图集可通过可变形匹配应用于整个或几乎整个头部的任何大小的图像，从而帮助从相关的相图像中分割出血管。此后使用的分割方法由拓扑图保留区域增长算法组成，该算法根据图集的当前区域使用自适应阈值。该算法通过迭代添加根据其灰度值及其附近值的变化选择的体素来构建动静脉树。通过在生长过程中仅选择简单的点，可以保证拓扑保护。该方法已在大脑的15个PC-MRA上执行。结果已使用MIP和3D表面渲染可视化进行了验证；与没有图集获得的其他结果的比较证明，基于图集的方法是优化血管分割策略的有效方法。

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