@misc{KnoetelSeidelZaslanskyetal.2017, author = {Kn{\"o}tel, David and Seidel, Ronald and Zaslansky, Paul and Prohaska, Steffen and Dean, Mason N. and Baum, Daniel}, title = {Automated Segmentation of Complex Patterns in Biological Tissues: Lessons from Stingray Tessellated Cartilage (Supplementary Material)}, doi = {10.12752/4.DKN.1.0}, year = {2017}, abstract = {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.}, language = {en} } @misc{HoerthBaumKnoeteletal.2015, author = {Hoerth, Rebecca M. and Baum, Daniel and Kn{\"o}tel, David and Prohaska, Steffen and Willie, Bettina M. and Duda, Georg and Hege, Hans-Christian and Fratzl, Peter and Wagermaier, Wolfgang}, title = {Registering 2D and 3D Imaging Data of Bone during Healing}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-53426}, year = {2015}, abstract = {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.}, language = {en} } @article{HoerthBaumKnoeteletal.2015, author = {Hoerth, Rebecca M. and Baum, Daniel and Kn{\"o}tel, David and Prohaska, Steffen and Willie, Bettina M. and Duda, Georg and Hege, Hans-Christian and Fratzl, Peter and Wagermaier, Wolfgang}, title = {Registering 2D and 3D Imaging Data of Bone during Healing}, volume = {56}, journal = {Connective Tissue Research}, number = {2}, publisher = {Taylor \& Francis}, doi = {10.3109/03008207.2015.1005210}, pages = {133 -- 143}, year = {2015}, language = {en} } @misc{KnoetelSeidelProhaskaetal.2017, author = {Kn{\"o}tel, David and Seidel, Ronald and Prohaska, Steffen and Dean, Mason N. and Baum, Daniel}, title = {Automated Segmentation of Complex Patterns in Biological Tissues: Lessons from Stingray Tessellated Cartilage}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-65785}, year = {2017}, abstract = {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.}, language = {en} } @article{KnoetelSeidelProhaskaetal.2017, author = {Kn{\"o}tel, David and Seidel, Ronald and Prohaska, Steffen and Dean, Mason N. and Baum, Daniel}, title = {Automated Segmentation of Complex Patterns in Biological Tissues: Lessons from Stingray Tessellated Cartilage}, journal = {PLOS ONE}, doi = {10.1371/journal.pone.0188018}, year = {2017}, abstract = {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.}, language = {en} }