Three-dimensional image analytical detection of intussusceptive pillars in murine lung
Three-dimensional image analytical detection of intussusceptive pillars in murine lung
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DOI：
10.1111/jmi.12300
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发表时间：
2015-12-01
影响因子：
2
通讯作者：
Konerding, M. A.
中科院分区：
文献类型：
--
作者：
Foehst, S.;Wagner, W.;Konerding, M. A.
多种疾病都可能导致肺组织的丢失。目前，这只能对症治疗。在小鼠身上，切除左肺后21天内可观察到完全代偿性肺生长。理解并将这种代偿性肺生长的概念转移到人类身上，将极大地改进治疗方案。肺的生长总是伴随着一个叫做血管生成的过程，从原有的毛细血管形成新的毛细血管。在肺生长过程中，可观察到毛细血管内的管腔内组织柱(套叠管柱)的形成。因此，柱状物可以理解为活跃的血管生成和微血管重塑的指标。因此，它们的检测对于描述代偿性肺生长的特征是非常有价值的。在血管腐蚀铸型中，这些柱子看起来像穿透血管的小孔。到目前为止，柱子的视觉检测仅基于2D图像。我们的方法依赖于高分辨率的同步加速器微计算机断层图像。在体素尺寸为370 nm的情况下，我们利用这种成像技术提供的空间信息，提出了第一个半自动检测肠套叠支柱的算法。至少半自动检测在肺部研究中是必不可少的，因为由于3D结构的复杂性和大小，手动柱子检测是不可行的。使用我们的算法，可以检测到数千个柱子并随后对其进行分析，例如，在可接受的人工交互数量下，关于它们的空间排列、大小和形状。在本文中，我们应用我们的新的柱子检测算法来计算不同试件的柱子密度。这些都是经过准备的，因此它们显示了不同的生长状态。通过比较相应的肺柱密度，可以研究肺随时间的增长情况。
A variety of diseases can lead to loss of lung tissue. Currently, this can be treated only symptomatically. In mice, a complete compensatory lung growth within 21 days after resection of the left lung can be observed. Understanding and transferring this concept of compensatory lung growth to humans would greatly improve therapeutic options. Lung growth is always accompanied by a process called angiogenesis forming new capillary blood vessels from preexisting ones. Among the processes during lung growth, the formation of transluminal tissue pillars within the capillary vessels (intussusceptive pillars) is observed. Therefore, pillars can be understood as an indicator for active angiogenesis and microvascular remodelling. Thus, their detection is very valuable when aiming at characterization of compensatory lung growth. In a vascular corrosion cast, these pillars appear as small holes that pierce the vessels. So far, pillars were detected visually only based on 2D images. Our approach relies on high-resolution synchrotron microcomputed tomographic images. With a voxel size of 370 nm we exploit the spatial information provided by this imaging technique and present the first algorithm to semiautomatically detect intussusceptive pillars. An at least semiautomatic detection is essential in lung research, as manual pillar detection is not feasible due to the complexity and size of the 3D structure. Using our algorithm, several thousands of pillars can be detected and subsequently analysed, e.g. regarding their spatial arrangement, size and shape with an acceptable amount of human interaction. In this paper, we apply our novel pillar detection algorithm to compute pillar densities of different specimens. These are prepared such that they show different growing states. Comparing the corresponding pillar densities allows to investigate lung growth over time.