RESUMEN
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.
RESUMEN
Premature fusion of craniofacial joints, i.e. sutures, is a major clinical condition. This condition affects children and often requires numerous invasive surgeries to correct. Minimally invasive external loading of the skull has shown some success in achieving therapeutic effects in a mouse model of this condition, promising a new non-invasive treatment approach. However, our fundamental understanding of the level of deformation that such loading has induced across the sutures, leading to the effects observed is severely limited, yet crucial for its scalability. We carried out a series of multiscale characterisations of the loading effects on normal and craniosynostotic mice, in a series of in vivo and ex vivo studies. This involved developing a custom loading setup as well as software for its control and a novel in situ CT strain estimation approach following the principles of digital volume correlation. Our findings highlight that this treatment may disrupt bone formation across the sutures through plastic deformation of the treated suture. The level of permanent deformations observed across the coronal suture after loading corresponded well with the apparent strain that was estimated. This work provides invaluable insight into the level of mechanical forces that may prevent early fusion of cranial joints during the minimally invasive treatment cycle and will help the clinical translation of the treatment approach to humans.