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Description: The cartilaginous endoskeletons of sharks and rays are covered by tiles of mineralized cartilage called tesserae that enclose areas of unmineralized cartilage. These tesselated layers are vital to the growth as well as the material properties of the skeleton, providing both flexibility and strength. An understanding of the principles behind the tiling of the mineralized layer requires a quantitative analysis of shark and ray skeletal tessellation. However, since a single skeletal element comprises several thousand tesserae, manual segmentation is infeasible. We developed an automated segmentation pipeline that, working from micro-CT data, allows quantification of all tesserae in a skeletal element in less than an hour. Our segmentation algorithm relies on aspects we have learned of general tesseral morphology. In micro-CT scans, tesserae usually appear as round or star-shaped plate-like tiles, wider than deep and connected by mineralized intertesseral joints. Based on these observations, we exploit the distance map of the mineralized layer to separate individual tiles using a hierarchical watershed algorithm. Utilizing a two-dimensional distance map that measures the distance in the plane of the mineralized layer only greatly improves the segmentation. We developed post-processing techniques to quickly correct segmentation errors in regions where tesseral shape differs from the assumed shape. Evaluation of our results is done qualitatively by visual comparison with raw datasets, and quantitatively by comparison to manual segmentations. Furthermore, we generate two-dimensional abstractions of the tiling network based on the neighborhood, allowing representation of complex, biological forms as simpler geometries. We apply our newly developed techniques to the analysis of the left and right hyomandibulae of four ages of stingray enabling the first quantitative analyses of the tesseral tiling structure, while clarifying how these patterns develop across ontogeny.

Description: The endoskeletons of sharks and rays are composed of an unmineralized cartilaginous core, covered in an outer layer of mineralized tiles called tesserae. The tessellated layer is vital to the growth as well as the material properties of the skeletal element, providing both flexibility and strength. However, characterizing the relationship between tesseral size and shape, and skeletal growth and mechanics is challenging because tesserae are small (a few hundred micrometers wide), anchored to the surrounding tissue in complex three-dimensional ways, and occur in huge numbers. Using a custom-made semi-automatic segmentation algorithm, we present the first quantitative and three-dimensional description of tesserae in micro-CT scans of whole skeletal elements. Our segmentation algorithm relies on aspects we have learned of general tesseral morphology. We exploit the distance map of the mineralized layer to separate individual tiles using a hierarchical watershed algorithm. Additionally, we have developed post-processing techniques to quickly correct segmentation errors. Our data reveals that the tessellation is not regular, with tesserae showing a great range of shapes, sizes and number of neighbors. This is partly region-dependent: for example, thick, columnar tesserae are arranged in series along convex edges with small radius of curvature (RoC), whereas more brick-or disc-shaped tesserae are found in planar areas. We apply our newly developed techniques on the left and right hyomandibula (skeletal elements supporting the jaws) from four different ages of a stingray species, to clarify how tiling patterns develop across ontogeny and differ within and between individuals. We evaluate the functional consequences of tesseral morphologies using finite element analysis and 3d-printing, for a better understanding of shark skeletal mechanics, but also to extract fundamental engineering design principles of tiling arrangements on load-bearing three-dimensional objects.

Description: The cartilaginous endoskeletons of Elasmobranchs (sharks and rays) are reinforced superficially by minute, mineralized tiles, called tesserae. Unlike the bony skeletons of other vertebrates, elasmobranch skeletons have limited healing capability and their tissues’ mechanisms for avoiding damage or managing it when it does occur are largely unknown. Here we describe an aberrant type of mineralized elasmobranch skeletal tissue called endophytic masses (EPMs), which grow into the uncalcified cartilage of the skeleton, but exhibit a strikingly different morphology compared to tesserae and other elasmobranch calcified tissues. We use biological and materials characterization techniques, including computed tomography, electron and light microscopy, x-ray and Raman spectroscopy and histology to characterize the morphology, ultrastructure and chemical composition of tesserae-associated EPMs in different elasmobranch species. EPMs appear to develop between and in intimate association with tesserae, but lack the lines of periodic growth and varying mineral density characteristic of tesserae. EPMs are mineral-dominated (high mineral and low organic content), comprised of birefringent bundles of large monetite or brushite crystals aligned end to end in long strings. Both Unusual skeletal mineralization in elasmobranchs tesserae and EPMs appear to develop in a type-2 collagen-based matrix, but in contrast to tesserae, all chondrocytes embedded or in contact with EPMs are dead and mineralized. The differences outlined between EPMs and tesserae demonstrate them to be distinct tissues. We discuss several possible reasons for EPM development, including tissue reinforcement, repair, and disruptions of mineralization processes, within the context of elasmobranch skeletal biology as well as descriptions of damage responses of other vertebrate mineralized tissues.

Description: The endoskeleton of sharks and rays (elasmobranchs) is comprised of a cartilaginous core, covered by thousands of mineralized tiles, called tesserae. Characterizing the relationship between tesseral morphometrics, skeletal growth and mechanics is challenging because tesserae are small (a few hundred micrometers wide), anchored to the surrounding tissue in complex three-dimensional ways, and occur in huge numbers. We integrate material property, histology, electron microscopy and synchrotron and laboratory µCT scans of skeletal elements from an ontogenetic series of round stingray Urobatis halleri, to gain insights into the generation and maintenance of a natural tessellated system. Using a custom-made semiautomatic segmentation algorithm, we present the first quantitative and 3d description of tesserae across whole skeletal elements. The tessellation is not interlocking or regular, with tesserae showing a great range of shapes, sizes and number of neighbors. This is partly region-dependent: for example, thick, columnar tesserae are arranged in series along convex edges with small radius of curvature (RoC), whereas more brick- or disc-shaped tesserae are found in planar/flatter areas. Comparison of the tessellation across ontogeny, shows that in younger animals, the forming tesseral network is less densely packed, appearing as a covering of separate, poorly mineralized islands that grow together with age to form a complete surface. Some gaps in the tessellation are localized to specific regions in all samples, indicating they are real features, perhaps either regions of delayed mineralization or of tendon insertion. We will use the structure of elasmobranch skeletons as a road map for understanding shark and ray skeletal mechanics, but also to extract fundamental engineering principles for tiled composite materials.

Description: Introduction – 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. Methods – 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. Results – 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.

Description: Various 3D imaging techniques are routinely used to examine biological materials, the results of which are usually a stack of grayscale images. In order to quantify structural aspects of the biological materials, however, they must first be extracted from the dataset in a process called segmentation. If the individual structures to be extracted are in contact or very close to each other, distance-based segmentation methods utilizing the Euclidean distance transform are commonly employed. Major disadvantages of the Euclidean distance transform, however, are its susceptibility to noise (very common in biological data), which often leads to incorrect segmentations (i.e. poor separation of objects of interest), and its limitation of being only effective for roundish objects. In the present work, we propose an alternative distance transform method, the random-walk distance transform, and demonstrate its effectiveness in high-throughput segmentation of three microCT datasets of biological tilings (i.e. structures composed of a large number of similar repeating units). In contrast to the Euclidean distance transform, this random-walk approach represents the global, rather than the local, geometric character of the objects to be segmented and, thus, is less susceptible to noise. In addition, it is directly applicable to structures with anisotropic shape characteristics. Using three case studies—stingray tessellated cartilage, starfish dermal endoskeleton, and the prismatic layer of bivalve mollusc shell—we provide a typical workflow for the segmentation of tiled structures, describe core image processing concepts that are underused in biological research, and show that for each study system, large amounts of biologically-relevant data can be rapidly segmented, visualized and analyzed.

Description: Various 3D imaging techniques are routinely used to examine biological materials, the results of which are usually a stack of grayscale images. In order to quantify structural aspects of the biological materials, however, they must first be extracted from the dataset in a process called segmentation. If the individual structures to be extracted are in contact or very close to each other, distance-based segmentation methods utilizing the Euclidean distance transform are commonly employed. Major disadvantages of the Euclidean distance transform, however, are its susceptibility to noise (very common in biological data), which often leads to incorrect segmentations (i.e. poor separation of objects of interest), and its limitation of being only effective for roundish objects. In the present work, we propose an alternative distance transform method, the random-walk distance transform, and demonstrate its effectiveness in high-throughput segmentation of three microCT datasets of biological tilings (i.e. structures composed of a large number of similar repeating units). In contrast to the Euclidean distance transform, this random-walk approach represents the global, rather than the local, geometric character of the objects to be segmented and, thus, is less susceptible to noise. In addition, it is directly applicable to structures with anisotropic shape characteristics. Using three case studies—stingray tessellated cartilage, starfish dermal endoskeleton, and the prismatic layer of bivalve mollusc shell—we provide a typical workflow for the segmentation of tiled structures, describe core image processing concepts that are underused in biological research, and show that for each study system, large amounts of biologically-relevant data can be rapidly segmented, visualized and analyzed.

Description: Introduction – 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. Methods – 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. Results – 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.

Description: Supplementary data to reproduce and understand key results from the related publication, including original image data and processed data. In particular, sections from hyomandibulae harvested from specimens of round stingray Urobatis halleri, donated from another study (DOI: 10.1002/etc.2564). Specimens were from sub-adults/adults collected by beach seine from collection sites in San Diego and Seal Beach, California, USA. The hyomandibulae were mounted in clay, sealed in ethanol-humidified plastic tubes and scanned with a Skyscan 1172 desktop μCT scanner (Bruker μCT, Kontich, Belgium) in association with another study (DOI: 10.1111/joa.12508). Scans for all samples were performed with voxel sizes of 4.89 μm at 59 kV source voltage and 167 μA source current, over 360◦ sample 120 rotation. For our segmentations, the datasets were resampled to a voxel size of 9.78 μm to reduce the size of the images and speed up processing. In addition, the processed data that was generated with the visualization software Amira with techniques described in the related publication based on the mentioned specimens.