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首页> 外文会议>SPIE Conference on Biomedical Applications in Molecular, Structural, and Functional Imaging >Vascular Landmark Detection in 3D CT Data
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Vascular Landmark Detection in 3D CT Data

机译：3D CT数据中的血管地标检测

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This work presents novel methods to accurately placing landmarks inside the vessel lumen. This task is an important prerequisite to automatic centerline tracing. Methods have been proposed in the past to determine the location of organ landmarks, and yet several challenges remain for vascular landmarks. First, placing landmarks inside the lumen could be challenging for narrow vessels. Second, contrast-enhanced arteries could be tightly surrounded by bones with similar intensity profiles, making detection difficult compared to arteries surrounded only by darker tissues. Third, landmarks not located at bifurcations could be ill-defined as they have high uncertainty in position. We first present a method to detect landmarks that are located at vessel bifurcations. Such landmarks have well-defined positions, and we detect them using machine learning techniques. We then present a method to detect vascular landmarks not located at bifurcations. First, a segment detector is created to detect a vessel segment. Annotating multiple points along a vessel segment is easier than annotating a single landmark position, as there is no well-defined position along a vessel. This resolves the ambiguity issue mentioned above. Second, spatial features are computed from the segment detector's response map, and a regression model is created which takes as input the local spatial features surrounding a voxel, and outputs a confidence score of how likely this voxel is inside the lumen. We evaluate the system on a set of 94 3D CT datasets.
机译：这项工作提供了准确地放置船腔内部地标的新方法。这项任务是自动中线跟踪的重要前提。过去已经提出了方法，以确定器官地标的位置，但血管地标仍有几个挑战。首先，将腔内的地标放置在狭窄的船上可能是挑战。其次，对比增强动脉可以被具有相似强度谱的骨骼紧密包围，与仅由较深组织包围的动脉相比，使检测难以。第三，不在分叉处的地标可能被判定，因为它们具有高不确定性的位置。我们首先提出一种检测位于船舶分叉的地标的方法。此类地标具有明确定义的位置，并使用机器学习技术检测它们。然后，我们提出了一种检测不位于分叉的血管地标的方法。首先，创建分段检测器以检测血管段。沿血管段注释多个点比注释单个地标位置更容易，因为沿着血管没有明确定义的位置。这解决了上述模糊问题。其次，从段检测器的响应图计算空间特征，并且创建了回归模型，其作为输入围绕体素周围的局部空间特征，并输出该体素在内腔内部的置信度得分。我们在一组94 3D CT数据集中评估系统。

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