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    <title language="eng">Biological strategies for fatique and wear avoidance: lessons from stingray skeletons and teeth</title>
    <parentTitle language="eng">Poster, Tomography for Scientific Advancement symposium (ToScA)</parentTitle>
    <enrichment key="PeerReviewed">no</enrichment>
    <author>Mason N. Dean</author>
    <submitter>Daniel Baum</submitter>
    <author>Ahmed Hosny</author>
    <author>Ronald Seidel</author>
    <author>Daniel Baum</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
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    <publishedYear>2020</publishedYear>
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    <language>eng</language>
    <pageFirst>115264</pageFirst>
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    <volume>134</volume>
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    <completedDate>2020-02-11</completedDate>
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    <title language="eng">Co-aligned chondrocytes: Zonal morphological variation and structured arrangement of cell lacunae in tessellated cartilage</title>
    <abstract language="eng">In most vertebrates the embryonic cartilaginous skeleton is replaced by bone during development. During this process, cartilage cells (chondrocytes) mineralize the extracellular matrix and undergo apoptosis, giving way to bone cells (osteocytes). In contrast, sharks and rays (elasmobranchs) have cartilaginous skeletons throughout life, where only the surface mineralizes, forming a layer of tiles (tesserae). Elasmobranch chondrocytes, unlike those of other vertebrates, survive cartilage mineralization and are maintained alive in spaces (lacunae) within tesserae. However, the function(s) of the chondrocytes in the mineralized tissue remain unknown. Applying a custom analysis workflow to high-resolution synchrotron microCT scans of tesserae, we characterize the morphologies and arrangements of stingray chondrocyte lacunae, using lacunar morphology as a proxy for chondrocyte morphology. We show that the cell density is comparable in unmineralized and mineralized tissue from our study species and that cells maintain the similar volume even when they have been incorporated into tesserae. This discovery supports previous hypotheses that elasmobranch chondrocytes, unlike those of other taxa, do not proliferate, hypertrophy or undergo apoptosis during mineralization. Tessera lacunae show zonal variation in their shapes—being flatter further from and more spherical closer to the unmineralized cartilage matrix and larger in the center of tesserae— and show pronounced organization into parallel layers and strong orientation toward neighboring tesserae. Tesserae also exhibit local variation in lacunar density, with the density considerably higher near pores passing through the tesseral layer, suggesting pores and cells interact (e.g. that pores contain a nutrient source). We hypothesize that the different lacunar types reflect the stages of the tesserae formation process, while also representing local variation in tissue architecture and cell function. Lacunae are linked by small passages (canaliculi) in the matrix to form elongate series at the tesseral periphery and tight clusters in the center of tesserae, creating a rich connectivity among cells. The network arrangement and the shape variation of chondrocytes in tesserae indicate that cells may interact within and between tesserae and manage mineralization differently from chondrocytes in other vertebrates, perhaps performing analogous roles to osteocytes in bone.</abstract>
    <parentTitle language="eng">Bone</parentTitle>
    <identifier type="doi">10.1016/j.bone.2020.115264</identifier>
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    <enrichment key="AcceptedDate">2020-02-03</enrichment>
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    <author>Júlia Chaumel</author>
    <submitter>Daniel Baum</submitter>
    <author>Merlind Schotte</author>
    <author>Joseph J. Bizzarro</author>
    <author>Paul Zaslansky</author>
    <author>Peter Fratzl</author>
    <author>Daniel Baum</author>
    <author>Mason N. Dean</author>
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    <id>7708</id>
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    <title language="eng">Co-aligned chondrocytes: Zonal morphological variation and structured arrangement of cell lacunae in tessellated cartilage</title>
    <abstract language="eng">In most vertebrates the embryonic cartilaginous skeleton is replaced by bone during development. During this process, cartilage cells (chondrocytes) mineralize the extracellular matrix and undergo apoptosis, giving way to bone cells (osteocytes). In contrast, sharks and rays (elasmobranchs) have cartilaginous skeletons throughout life, where only the surface mineralizes, forming a layer of tiles (tesserae). Elasmobranch chondrocytes, unlike those of other vertebrates, survive cartilage mineralization and are maintained alive in spaces (lacunae) within tesserae. However, the function(s) of the chondrocytes in the mineralized tissue remain unknown. Applying a custom analysis workflow to high-resolution synchrotron microCT scans of tesserae, we characterize the morphologies and arrangements of stingray chondrocyte lacunae, using lacunar morphology as a proxy for chondrocyte morphology. We show that the cell density is comparable in unmineralized and mineralized tissue from our study species and that cells maintain the similar volume even when they have been incorporated into tesserae. This discovery supports previous hypotheses that elasmobranch chondrocytes, unlike those of other taxa, do not proliferate, hypertrophy or undergo apoptosis during mineralization. Tessera lacunae show zonal variation in their shapes—being flatter further from and more spherical closer to the unmineralized cartilage matrix and larger in the center of tesserae— and show pronounced organization into parallel layers and strong orientation toward neighboring tesserae. Tesserae also exhibit local variation in lacunar density, with the density considerably higher near pores passing through the tesseral layer, suggesting pores and cells interact (e.g. that pores contain a nutrient source). We hypothesize that the different lacunar types reflect the stages of the tesserae formation process, while also representing local variation in tissue architecture and cell function. Lacunae are linked by small passages (canaliculi) in the matrix to form elongate series at the tesseral periphery and tight clusters in the center of tesserae, creating a rich connectivity among cells. The network arrangement and the shape variation of chondrocytes in tesserae indicate that cells may interact within and between tesserae and manage mineralization differently from chondrocytes in other vertebrates, perhaps performing analogous roles to osteocytes in bone.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-77087</identifier>
    <author>Júlia Chaumel</author>
    <submitter>Daniel Baum</submitter>
    <author>Merlind Schotte</author>
    <author>Joseph J. Bizzarro</author>
    <author>Paul Zaslansky</author>
    <author>Peter Fratzl</author>
    <author>Daniel Baum</author>
    <author>Mason N. Dean</author>
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      <title>ZIB-Report</title>
      <number>20-04</number>
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    <collection role="persons" number="baum">Baum, Daniel</collection>
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    <file>https://opus4.kobv.de/opus4-zib/files/7708/ZR-20-04.pdf</file>
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  <doc>
    <id>7822</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
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    <language>eng</language>
    <pageFirst>100905</pageFirst>
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    <volume>7</volume>
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    <completedDate>2020-05-01</completedDate>
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    <title language="eng">Image analysis pipeline for segmentation of a biological porosity network, the lacuno-canalicular system in stingray tesserae</title>
    <abstract language="eng">A prerequisite for many analysis tasks in modern comparative biology is the segmentation of 3-dimensional (3D) images of the specimens being investigated (e.g. from microCT data). Depending on the specific imaging technique that was used to acquire the images and on the image resolution, different segmentation tools will be required. While some standard tools exist that can often be applied for specific subtasks, building whole processing pipelines solely from standard tools is often difficult. Some tasks may even necessitate the implementation of manual interaction tools to achieve a quality that is sufficient for the subsequent analysis. In this work, we present a pipeline of segmentation tools that can be used for the semi-automatic segmentation and quantitative analysis of voids in tissue (i.e. internal structural porosity). We use this pipeline to analyze lacuno-canalicular networks in stingray tesserae from 3D images acquired with synchrotron microCT.&#13;
* The first step of this processing pipeline, the segmentation of the tesserae, was performed using standard marker-based watershed segmentation. The efficient processing of the next two steps, that is, the segmentation of all lacunae spaces belonging to a specific tessera and the separation of these spaces into individual lacunae required modern, recently developed tools.&#13;
* For proofreading, we developed a graph-based interactive method that allowed us to quickly split lacunae that were accidentally merged, and to merge lacunae that were wrongly split.&#13;
* Finally, the tesserae and their corresponding lacunae were subdivided into anatomical regions of interest (structural wedges) using a semi- manual approach.</abstract>
    <parentTitle language="eng">MethodsX</parentTitle>
    <identifier type="doi">10.1016/j.mex.2020.100905</identifier>
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    <enrichment key="AcceptedDate">2020-04-23</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-78237</enrichment>
    <author>Merlind Schotte</author>
    <submitter>Daniel Baum</submitter>
    <author>Júlia Chaumel</author>
    <author>Mason N. Dean</author>
    <author>Daniel Baum</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
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  <doc>
    <id>7823</id>
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    <language>eng</language>
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    <publishedDate>2020-04-23</publishedDate>
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    <title language="eng">Image analysis pipeline for segmentation of a biological porosity network, the lacuno-canalicular system in stingray tesserae</title>
    <abstract language="eng">A prerequisite for many analysis tasks in modern comparative biology is the segmentation of 3-dimensional (3D) images of the specimens being investigated (e.g. from microCT data). Depending on the specific imaging technique that was used to acquire the images and on the image resolution, different segmentation tools will be required. While some standard tools exist that can often be applied for specific subtasks, building whole processing pipelines solely from standard tools is often difficult. Some tasks may even necessitate the implementation of manual interaction tools to achieve a quality that is sufficient for the subsequent analysis. In this work, we present a pipeline of segmentation tools that can be used for the semi-automatic segmentation and quantitative analysis of voids in tissue (i.e. internal structural porosity). We use this pipeline to analyze lacuno-canalicular networks in stingray tesserae from 3D images acquired with synchrotron microCT.&#13;
* The first step of this processing pipeline, the segmentation of the tesserae, was performed using standard marker-based watershed segmentation. The efficient processing of the next two steps, that is, the segmentation of all lacunae spaces belonging to a specific tessera and the separation of these spaces into individual lacunae required modern, recently developed tools.&#13;
* For proofreading, we developed a graph-based interactive method that allowed us to quickly split lacunae that were accidentally merged, and to merge lacunae that were wrongly split.&#13;
* Finally, the tesserae and their corresponding lacunae were subdivided into anatomical regions of interest (structural wedges) using a semi- manual approach.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-78237</identifier>
    <author>Merlind Schotte</author>
    <submitter>Daniel Baum</submitter>
    <author>Júlia Chaumel</author>
    <author>Mason N. Dean</author>
    <author>Daniel Baum</author>
    <series>
      <title>ZIB-Report</title>
      <number>20-12</number>
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    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="vissys">Image Analysis in Biology and Materials Science</collection>
    <collection role="persons" number="baum">Baum, Daniel</collection>
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    <file>https://opus4.kobv.de/opus4-zib/files/7823/ZR-20-12.pdf</file>
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  <doc>
    <id>6652</id>
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    <publishedYear>2017</publishedYear>
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    <language>eng</language>
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    <title language="eng">Automated Segmentation of Complex Patterns in Biological Tissues: Lessons from Stingray Tessellated Cartilage (Supplementary Material)</title>
    <abstract language="eng">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.</abstract>
    <identifier type="doi">10.12752/4.DKN.1.0</identifier>
    <note>Supplementary data to reproduce and understand key results from the related publication, including original image data and processed data.</note>
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    <enrichment key="SoftwareDescription">The data was processed with the visualization software Amira. See http://www.zib.de/software/tesserae-segmentation for information about Amira and how to download the Amira extension package created for this publication. See https://github.com/zibamira/tesserae-segmentation.git for the source code of the Amira extension package.</enrichment>
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    <author>David Knötel</author>
    <submitter>David Knötel</submitter>
    <author>Ronald Seidel</author>
    <author>Paul Zaslansky</author>
    <author>Steffen Prohaska</author>
    <author>Mason N. Dean</author>
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    <completedDate>2019-07-08</completedDate>
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    <title language="eng">High-Throughput Segmentation of Tiled Biological Structures using Random-Walk Distance Transforms</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Integrative And Comparative Biology</parentTitle>
    <identifier type="doi">10.1093/icb/icz117</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2019-06-19</enrichment>
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    <author>Daniel Baum</author>
    <submitter>Daniel Baum</submitter>
    <author>James C. Weaver</author>
    <author>Igor Zlotnikov</author>
    <author>David Knötel</author>
    <author>Lara Tomholt</author>
    <author>Mason N. Dean</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
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    <publishedDate>2019-07-04</publishedDate>
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    <title language="eng">High-Throughput Segmentation of Tiled Biological Structures using Random-Walk Distance Transforms</title>
    <abstract language="eng">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.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-73841</identifier>
    <author>Daniel Baum</author>
    <submitter>Daniel Baum</submitter>
    <author>James C. Weaver</author>
    <author>Igor Zlotnikov</author>
    <author>David Knötel</author>
    <author>Lara Tomholt</author>
    <author>Mason N. Dean</author>
    <series>
      <title>ZIB-Report</title>
      <number>19-33</number>
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    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="vissys">Image Analysis in Biology and Materials Science</collection>
    <collection role="persons" number="baum">Baum, Daniel</collection>
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    <title language="eng">To build a shark: 3D tiling laws of tessellated cartilage</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Abstract in Integrative and Comparative Biology; conference Society of Integrative and Comparative Biology annual meeting, January 3-7, 2016, Portland, USA</parentTitle>
    <identifier type="url">https://academic.oup.com/icb/article-pdf/56/suppl_1/e1/9102603/icw002.pdf</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <author>Mason N. Dean</author>
    <submitter>David Knötel</submitter>
    <author>R. Seidel</author>
    <author>David Knötel</author>
    <author>K. Lyons</author>
    <author>Daniel Baum</author>
    <author>James C. Weaver</author>
    <author>Peter Fratzl</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="vissys">Image Analysis in Biology and Materials Science</collection>
    <collection role="persons" number="baum">Baum, Daniel</collection>
    <collection role="persons" number="knoetel">Knötel, David</collection>
    <collection role="projects" number="TESSERAE">TESSERAE</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>5821</id>
    <completedYear/>
    <publishedYear>2015</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>poster</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Segmentation of the Tessellated Mineralized Endoskeleton of Sharks and Rays</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Poster, Tomography for Scientific Advancement symposium (ToScA), Manchester, UK, September 3 - 4, 2015</parentTitle>
    <author>David Knötel</author>
    <submitter>David Knötel</submitter>
    <author>Ronald Seidel</author>
    <author>James C. Weaver</author>
    <author>Daniel Baum</author>
    <author>Mason N. Dean</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="persons" number="baum">Baum, Daniel</collection>
    <collection role="persons" number="knoetel">Knötel, David</collection>
    <collection role="projects" number="TESSERAE">TESSERAE</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
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