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Automated 3D Segmentation of Multiple Surfaces with a Shared Hole:Segmentation of the Neural Canal Opening in SD-OCT Volumes

机译:具有共享孔的多个表面的自动3D分割:SD-OCT卷中神经管开口的分割

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摘要

The need to segment multiple interacting surfaces is a common problem in medical imaging and it is often assumed that such surfaces are continuous within the confines of the region of interest. However, in some application areas, the surfaces of interest may contain a shared hole in which the surfaces no longer exist and the exact location of the hole boundary is not known a priori. The boundary of the neural canal opening seen in spectral-domain optical coherence tomography volumes is an example of a “hole” embedded with multiple surrounding surfaces. Segmentation approaches that rely on finding the surfaces alone are prone to failures as deeper structures within the hole can “attract” the surfaces and pull them away from their correct location at the hole boundary. With this application area in mind, we present a graph-theoretic approach for segmenting multiple surfaces with a shared hole. The overall cost function that is optimized consists of both the costs of the surfaces outside the hole and the cost of boundary of the hole itself. The constraints utilized were appropriately adapted in order to ensure the smoothness of the hole boundary in addition to ensuring the smoothness of the non-overlapping surfaces. By using this approach, a significant improvement was observed over a more traditional two-pass approach in which the surfaces are segmented first (assuming the presence of no hole) followed by segmentingthe neural canal opening.
机译:分割多个相互作用的表面的需要是医学成像中的常见问题,并且通常假定这样的表面在感兴趣区域的范围内是连续的。但是,在某些应用领域中,感兴趣的表面可能包含一个共享的孔,该表面中不再存在这些孔,并且先验未知孔边界的确切位置。在光谱域光学相干断层扫描体积中看到的神经管开口的边界是嵌有多个周围表面的“孔”的一个示例。仅依赖于查找表面的分割方法很容易发生故障,因为孔内较深的结构可以“吸引”表面并将其拉离孔边界处的正确位置。考虑到这个应用领域,我们提出了一种图形理论的方法来分割具有共享孔的多个表面。优化的总成本函数包括孔外部曲面的成本和孔本身边界的成本。为了确保孔边界的平滑度,除了确保不重叠的表面的平滑度之外,还适当地利用了所使用的约束。通过使用这种方法,与传统的两次通过方法相比,观察到了显着的改进,在传统的两次通过方法中,首先对表面进行了分割(假设不存在孔),然后进行了分割神经管开口。

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