Schotte Merlind, Chaumel Júlia, Dean Mason N, Baum Daniel
Visual Data Analysis Department, Zuse Institute Berlin, Takustrasse 7, 14195 Berlin, Germany.
Department of Biomaterials, Max Planck Institute of Colloids and Interfaces, Am Mühlenberg 1, 14476 Potsdam, Germany.
MethodsX. 2020 May 1;7:100905. doi: 10.1016/j.mex.2020.100905. eCollection 2020.
A prerequisite for many analysis tasks in modern comparative biology is the segmentation of 3-dimensional (3D) images of the specimens being investigated (e.g. from microCT data). Depending on the specific imaging technique that was used to acquire the images and on the image resolution, different segmentation tools are required. While some standard tools exist that can often be applied for specific subtasks, building whole processing pipelines solely from standard tools is often difficult. Some tasks may even necessitate the implementation of manual interaction tools to achieve a quality that is sufficient for subsequent analysis. In this work, we present a pipeline of segmentation tools that can be used for the semiautomatic segmentation and quantitative analysis of voids in tissue (i.e. internal structural porosity). We use this pipeline to analyze lacuno-canalicular networks in stingray tesserae from 3D images acquired with synchrotron microCT.•The first step of this pipeline, the segmentation of the tesserae, was performed using standard marker-based watershed segmentation.•The efficient processing of the next two steps, that is, the segmentation of all lacunae spaces belonging to a specific tessera and the separation of these spaces into individual lacunae required recently developed, novel tools.•For error correction, we developed an interactive method that allowed us to quickly split lacunae that were accidentally merged, and to merge lacunae that were wrongly split.•Finally, the tesserae and their corresponding lacunae were subdivided into structural wedges (i.e. specific anatomical regions) using a semi-manual approach. With this processing pipeline, analysis of a variety of interconnected structural networks (e.g. vascular or lacuno-canalicular networks) can be achieved in a comparatively high-throughput fashion. In our study system, we were able to efficiently segment more than 12,000 lacunae in high-resolution scans of nine tesserae, providing a robust data set for statistical analysis.
现代比较生物学中许多分析任务的一个前提条件是对所研究标本的三维（3D）图像进行分割（例如从显微CT数据中获取）。根据用于获取图像的特定成像技术以及图像分辨率，需要不同的分割工具。虽然存在一些标准工具，通常可用于特定的子任务，但仅用标准工具构建整个处理流程往往很困难。有些任务甚至可能需要实现手动交互工具，以达到足以进行后续分析的质量。在这项工作中，我们展示了一个分割工具流程，可用于组织中孔隙（即内部结构孔隙率）的半自动分割和定量分析。我们使用这个流程来分析用同步加速器显微CT获取的3D图像中的黄貂鱼骨板中的腔隙小管网络。•该流程的第一步，即骨板的分割，使用基于标准标记的分水岭分割法进行。•接下来两步的高效处理，即属于特定骨板的所有腔隙空间的分割以及将这些空间分离为单个腔隙，需要最近开发的新颖工具。•为了进行误差校正，我们开发了一种交互式方法，使我们能够快速分割意外合并的腔隙，并合并错误分割的腔隙。•最后，使用半自动方法将骨板及其相应的腔隙细分为结构楔（即特定的解剖区域）。通过这个处理流程，可以以相对高通量的方式实现对各种相互连接的结构网络（如血管或腔隙小管网络）的分析。在我们的研究系统中，我们能够在九个骨板的高分辨率扫描中高效分割超过12,000个腔隙，为统计分析提供了一个可靠的数据集。
