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Combining atlas and active contour for automatic 3D medical image segmentation

机译:结合图集和活动轮廓进行自动3D医学图像分割

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Atlas based methods and active contours are two families of techniques widely used for the task of 3D medical image segmentation. In this work we present a coupled framework where the two methods are combined together, in order to exploit each's advantage while avoid their respective drawbacks. Indeed, the atlas based methods lacks the flexibility in locally tuning the segmentation boundary; whereas the active contour has the drawback that the final result heavily depends on the initialization as well as the contour evolution energy functional. Therefore, in the proposed work, the atlas based segmentation provides a probability map, which not only supplies the initial contour position, but also defines the contour evolution energy in an on-line fashion. Afterward, the active contour further converges to the desired object boundary. Finally, the method is tested on various 3D medical images to demonstrate its robustness as well as accuracy.
机译:基于地图集的方法和活动轮廓是广泛用于3D医学图像分割任务的两种技术。在这项工作中,我们提出了一个耦合的框架,将两种方法结合在一起,以利用每种方法的优点,同时避免它们各自的缺点。实际上,基于图集的方法在局部调整分割边界时缺乏灵活性。主动轮廓的缺点是最终结果在很大程度上取决于初始化以及轮廓演化能量函数。因此,在提出的工作中,基于图集的分割提供了概率图,该图不仅提供初始轮廓位置,而且以在线方式定义轮廓演化能量。然后,活动轮廓进一步收敛到所需的对象边界。最后,该方法在各种3D医学图像上进行了测试,以证明其鲁棒性和准确性。

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