Faculty of Information and Communication Technology, Mahidol University, Nakhonpathom, Thailand; Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
Faculty of Information and Communication Technology, Mahidol University, Nakhonpathom, Thailand.
Comput Biol Med. 2019 Apr;107:73-85. doi: 10.1016/j.compbiomed.2019.01.027. Epub 2019 Feb 6.
A 3D model of abdominal aortic aneurysm (AAA) can provide useful anatomical information for clinical management and simulation. Thin-slice contiguous computed tomographic (CT) angiography is the best source of medical images for construction of 3D models, which requires segmentation of AAA in the images. Existing methods for segmentation of AAA rely on either manual process or 2D segmentation in each 2D CT slide. However, a traditional manual segmentation is a time consuming process which is not practical for routine use. The construction of a 3D model from 2D segmentation of each CT slice is not a fully satisfactory solution due to rough contours that can occur because of lack of constraints among segmented slices, as well as missed segmentation slices. To overcome such challenges, this paper proposes the 3D segmentation of AAA using the concept of variable neighborhood search by iteratively alternating between two different segmentation techniques in the two different 3D search spaces of voxel intensity and voxel gradient. The segmentation output of each method is used as the initial contour to the other method in each iteration. By alternating between search spaces, the technique can escape local minima that naturally occur in each search space. Also, the 3D search spaces provide more constraints across CT slices, when compared with the 2D search spaces in individual CT slices. The proposed method is evaluated with 10 easy and 10 difficult cases of AAA. The results show that the proposed 3D segmentation technique achieves the outstanding segmentation accuracy with an average dice similarity value (DSC) of 91.88%, when compared to the other methods using the same dataset, which are the 2D proposed method, classical graph cut, distance regularized level set evolution, and registration based geometric active contour with the DSCs of 87.57 ± 4.52%, 72.47 ± 8.11%, 58.50 ± 8.86% and 76.21 ± 10.49%, respectively.
腹主动脉瘤（AAA）的 3D 模型可为临床管理和模拟提供有用的解剖学信息。薄层连续计算机断层血管造影（CT）是构建 3D 模型的最佳医学图像来源，这需要对图像中的 AAA 进行分割。现有的 AAA 分割方法依赖于手动过程或在每个 2D CT 幻灯片中进行 2D 分割。然而，传统的手动分割是一个耗时的过程，对于常规使用来说并不实际。由于缺乏分段切片之间的约束以及错过的分段切片，从每个 CT 切片的 2D 分段构建 3D 模型并不是一个完全令人满意的解决方案。为了克服这些挑战，本文提出了一种使用变邻域搜索的 AAA 3D 分割方法，该方法在体素强度和体素梯度的两个不同 3D 搜索空间中交替使用两种不同的分割技术。在每次迭代中，将每种方法的分割输出用作另一种方法的初始轮廓。通过在搜索空间之间交替，该技术可以避免每个搜索空间中自然出现的局部最小值。此外，与单个 CT 切片中的 2D 搜索空间相比，3D 搜索空间提供了更多的 CT 切片之间的约束。使用相同数据集评估了所提出的方法对 10 个易和 10 个难的 AAA 病例的效果。结果表明，与使用相同数据集的其他方法相比，所提出的 3D 分割技术具有出色的分割准确性，平均骰子相似系数（DSC）为 91.88%，而其他方法分别为 2D 提出的方法、经典图割、距离正则化水平集演化和基于配准的几何主动轮廓，其 DSC 值分别为 87.57 ± 4.52%、72.47 ± 8.11%、58.50 ± 8.86%和 76.21 ± 10.49%。
