Knötel David, Seidel Ronald, Prohaska Steffen, Dean Mason N, Baum Daniel
Zuse Institute Berlin, Dept. of Visual Data Analysis, Berlin, Germany.
Max Planck Institute of Colloids and Interfaces, Dept. of Biomaterials, Potsdam-Golm, Germany.
PLoS One. 2017 Dec 13;12(12):e0188018. doi: 10.1371/journal.pone.0188018. eCollection 2017.
Many biological structures show recurring tiling patterns on one structural level or the other. Current image acquisition techniques are able to resolve those tiling patterns to allow quantitative analyses. The resulting image data, however, may contain an enormous number of elements. This renders manual image analysis infeasible, in particular when statistical analysis is to be conducted, requiring a larger number of image data to be analyzed. As a consequence, the analysis process needs to be automated to a large degree. In this paper, we describe a multi-step image segmentation pipeline for the automated segmentation of the calcified cartilage into individual tesserae from computed tomography images of skeletal elements of stingrays.
Besides applying state-of-the-art algorithms like anisotropic diffusion smoothing, local thresholding for foreground segmentation, distance map calculation, and hierarchical watershed, we exploit a graph-based representation for fast correction of the segmentation. In addition, we propose a new distance map that is computed only in the plane that locally best approximates the calcified cartilage. This distance map drastically improves the separation of individual tesserae. We apply our segmentation pipeline to hyomandibulae from three individuals of the round stingray (Urobatis halleri), varying both in age and size.
Each of the hyomandibula datasets contains approximately 3000 tesserae. To evaluate the quality of the automated segmentation, four expert users manually generated ground truth segmentations of small parts of one hyomandibula. These ground truth segmentations allowed us to compare the segmentation quality w.r.t. individual tesserae. Additionally, to investigate the segmentation quality of whole skeletal elements, landmarks were manually placed on all tesserae and their positions were then compared to the segmented tesserae. With the proposed segmentation pipeline, we sped up the processing of a single skeletal element from days or weeks to a few hours.
许多生物结构在一个或多个结构层次上呈现出重复的平铺模式。当前的图像采集技术能够解析这些平铺模式，以便进行定量分析。然而，由此产生的图像数据可能包含大量元素。这使得手动图像分析变得不可行，特别是当要进行统计分析时，需要分析大量的图像数据。因此，分析过程需要在很大程度上实现自动化。在本文中，我们描述了一种多步骤图像分割流程，用于从黄貂鱼骨骼元素的计算机断层扫描图像中自动将钙化软骨分割成单个镶嵌块。
除了应用诸如各向异性扩散平滑、用于前景分割的局部阈值处理、距离图计算和分层分水岭等先进算法外，我们还利用基于图的表示来快速校正分割。此外，我们提出了一种新的距离图，它仅在局部最接近钙化软骨的平面中计算。这种距离图极大地改善了单个镶嵌块的分离。我们将我们的分割流程应用于来自三只圆尾黄貂鱼（Urobatis halleri）个体的舌颌骨，这些个体在年龄和大小上各不相同。
每个舌颌骨数据集包含大约3000个镶嵌块。为了评估自动分割的质量，四名专家用户手动生成了一个舌颌骨小部分的真实分割。这些真实分割使我们能够比较单个镶嵌块的分割质量。此外，为了研究整个骨骼元素的分割质量，在所有镶嵌块上手动放置地标，然后将它们的位置与分割后的镶嵌块进行比较。通过提出的分割流程，我们将单个骨骼元素的处理时间从数天或数周加快到了几个小时。
