Department of Mechanical Engineering, University College London, London, UK.
Developmental Biology and Cancer Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, University College London, London, UK.
J Anat. 2024 Dec;245(6):829-841. doi: 10.1111/joa.14063. Epub 2024 May 17.
X-ray Computed Tomography (CT) images are widely used in various fields of natural, physical, and biological sciences. 3D reconstruction of the images involves segmentation of the structures of interest. Manual segmentation has been widely used in the field of biological sciences for complex structures composed of several sub-parts and can be a time-consuming process. Many tools have been developed to automate the segmentation process, all with various limitations and advantages, however, multipart segmentation remains a largely manual process. The aim of this study was to develop an open-access and user-friendly tool for the automatic segmentation of calcified tissues, specifically focusing on craniofacial bones. Here we describe BounTI, a novel segmentation algorithm which preserves boundaries between separate segments through iterative thresholding. This study outlines the working principles behind this algorithm, investigates the effect of several input parameters on its outcome, and then tests its versatility on CT images of the craniofacial system from different species (e.g. a snake, a lizard, an amphibian, a mouse and a human skull) with various scan qualities. The case studies demonstrate that this algorithm can be effectively used to segment the craniofacial system of a range of species automatically. High-resolution microCT images resulted in more accurate boundary-preserved segmentation, nonetheless significantly lower-quality clinical images could still be segmented using the proposed algorithm. Methods for manual intervention are included in this tool when the scan quality is insufficient to achieve the desired segmentation results. While the focus here was on the craniofacial system, BounTI can be used to automatically segment any hard tissue. The tool presented here is available as an Avizo/Amira add-on, a stand-alone Windows executable, and a Python library. We believe this accessible and user-friendly segmentation tool can benefit the wider anatomical community.
X 射线计算机断层扫描（CT）图像广泛应用于自然、物理和生物科学的各个领域。图像的 3D 重建涉及到感兴趣结构的分割。手动分割在由几个子部分组成的复杂结构的生物科学领域得到了广泛应用，但这是一个耗时的过程。已经开发了许多工具来实现分割过程的自动化，这些工具都具有各种局限性和优势，但多部分分割仍然主要是手动过程。本研究旨在开发一种用于自动分割钙化组织的开放获取和用户友好的工具，特别是针对颅面骨骼。在这里，我们描述了 BounTI，这是一种新的分割算法，通过迭代阈值处理来保留单独部分之间的边界。本研究概述了该算法背后的工作原理，研究了几个输入参数对其结果的影响，然后测试了其在来自不同物种（如蛇、蜥蜴、两栖动物、老鼠和人类颅骨）的颅面系统 CT 图像上的多功能性，这些图像具有不同的扫描质量。案例研究表明，该算法可有效地用于自动分割一系列物种的颅面系统。高分辨率微 CT 图像导致更准确的边界保留分割，但即使使用所提出的算法，质量较低的临床图像仍然可以分割。当扫描质量不足以获得所需的分割结果时，此工具中包含了手动干预的方法。虽然这里的重点是颅面系统，但 BounTI 可用于自动分割任何硬组织。这里提供的工具是作为 Avizo/Amira 的附加组件、独立的 Windows 可执行文件和 Python 库提供的。我们相信，这个易于访问和用户友好的分割工具将使更广泛的解剖学社区受益。
