High-Throughput Segmentation of Tiled Biological Structures using Random-Walk Distance Transforms.

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, the 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-tessellated cartilage from a stingray, the dermal endoskeleton of a starfish, and the prismatic layer of a 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.

Automated segmentation of complex patterns in biological tissues: Lessons from stingray tessellated cartilage.

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.

Ultrastructural, material and crystallographic description of endophytic masses - A possible damage response in shark and ray tessellated calcified cartilage.

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 materials and biological tissue 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 calcium phosphate crystals (likely brushite) aligned end to end in long strings. Both 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 damage responses of other vertebrate mineralized tissues.

Registering 2D and 3D imaging data of bone during healing.

UNLABELLED: PURPOSE/AIMS OF THE STUDY: Bone's hierarchical structure can be visualized using a variety of methods. Many techniques, such as light and electron microscopy generate two-dimensional (2D) images, while micro-computed tomography (µCT) allows a direct representation of the three-dimensional (3D) structure. In addition, different methods provide complementary structural information, such as the arrangement of organic or inorganic compounds. The overall aim of the present study is to answer bone research questions by linking information of different 2D and 3D imaging techniques. A great challenge in combining different methods arises from the fact that they usually reflect different characteristics of the real structure. MATERIALS AND METHODS: We investigated bone during healing by means of µCT and a couple of 2D methods. Backscattered electron images were used to qualitatively evaluate the tissue's calcium content and served as a position map for other experimental data. Nanoindentation and X-ray scattering experiments were performed to visualize mechanical and structural properties. RESULTS: We present an approach for the registration of 2D data in a 3D µCT reference frame, where scanning electron microscopies serve as a methodic link. Backscattered electron images are perfectly suited for registration into µCT reference frames, since both show structures based on the same physical principles. We introduce specific registration tools that have been developed to perform the registration process in a semi-automatic way. CONCLUSIONS: By applying this routine, we were able to exactly locate structural information (e.g. mineral particle properties) in the 3D bone volume. In bone healing studies this will help to better understand basic formation, remodeling and mineralization processes.