TY - GEN A1 - Knötel, David A1 - Seidel, Ronald A1 - Prohaska, Steffen A1 - Dean, Mason N. A1 - Baum, Daniel T1 - Automated Segmentation of Complex Patterns in Biological Tissues: Lessons from Stingray Tessellated Cartilage N2 - 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. T3 - ZIB-Report - 17-62 KW - micro-CT KW - image segmentation KW - 2D distance map KW - hierarchical watershed KW - stingray KW - tesserae KW - biological tilings KW - Amira Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-65785 SN - 1438-0064 ER - TY - JOUR A1 - Knötel, David A1 - Seidel, Ronald A1 - Prohaska, Steffen A1 - Dean, Mason N. A1 - Baum, Daniel T1 - Automated Segmentation of Complex Patterns in Biological Tissues: Lessons from Stingray Tessellated Cartilage JF - PLOS ONE N2 - 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. Y1 - 2017 U6 - https://doi.org/10.1371/journal.pone.0188018 ER - TY - GEN A1 - Knötel, David A1 - Seidel, Ronald A1 - Zaslansky, Paul A1 - Prohaska, Steffen A1 - Dean, Mason N. A1 - Baum, Daniel T1 - Automated Segmentation of Complex Patterns in Biological Tissues: Lessons from Stingray Tessellated Cartilage (Supplementary Material) N2 - 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. Y1 - 2017 U6 - https://doi.org/10.12752/4.DKN.1.0 N1 - Supplementary data to reproduce and understand key results from the related publication, including original image data and processed data. ER - TY - GEN A1 - Bertelmann, Roland A1 - Koch, Thorsten A1 - Ceynowa, Klaus A1 - Söllner, Konstanze A1 - Christof, Jürgen A1 - Rusch, Beate A1 - Schäffler, Hildegard A1 - Putnings, Markus A1 - Pampel, Heinz A1 - Kuberek, Monika A1 - Boltze, Julia A1 - Lohrum, Stefan A1 - Retter, Regina A1 - Höllerl, Annika A1 - Faensen, Katja A1 - Steffen, Ronald A1 - Gross, Matthias A1 - Hoffmann, Cornelia A1 - Haoua, Marsa T1 - DeepGreen: Etablierung und Weiterentwicklung rechtssicherer Workflows zur effizienten Umsetzung von Open-Access-Komponenten in Lizenzvereinbarungen für wissenschaftliche Publikationen – Abschlussbericht N2 - DeepGreen wurde vom 01.08.2018 bis zum 30.06.2021 in einer zweiten Projektphase von der Deutschen Forschungsgemeinschaft (DFG) gefördert. DeepGreen unterstützt Bibliotheken als Dienstleister für Hochschulen, außeruniversitäre Forschungseinrichtungen und die dort tätigen Wissenschaftler:innen dabei, Publikationen auf Open-Access-Repositorien frei zugänglich zu machen und fördert das Zusammenspiel von wissenschaftlichen Einrichtungen und Verlagen. An der zweiten Projektphase waren der Kooperative Bibliotheksverbund Berlin-Brandenburg, die Bayerische Staatsbibliothek, der Bibliotheksverbund Bayern, die Universitätsbibliotheken der Friedrich-Alexander-Universität Erlangen-Nürnberg und der Technischen Universität Berlin und das Helmholtz Open Science Office beteiligt. In dem Projekt wurde erfolgreich eine technische und organisatorische Lösung zur automatisierten Verteilung von Artikeldaten wissenschaftlicher Verlage an institutionelle und fachliche Repositorien entwickelt. In der zweiten Projektphase lag der Fokus auf der Erprobung der Datendrehscheibe in der Praxis und der Ausweitung auf weitere Datenabnehmer und weitere Verlage. Im Anschluss an die DFG-geförderte Projektlaufzeit ist DeepGreen in einen zweijährigen Pilotbetrieb übergegangen. Ziel des Pilotbetriebs ist es, den Übergang in einen bundesweiten Real-Betrieb vorzubereiten. T3 - ZIB-Report - 21-37 KW - Open Access KW - Repositorien KW - DeepGreen Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-85420 SN - 1438-0064 ER - TY - GEN A1 - Bertelmann, Roland A1 - Boltze, Julia A1 - Ceynowa, Klaus A1 - Christof, Jürgen A1 - Faensen, Katja A1 - Groß, Matthias A1 - Hoffmann, Cornelia A1 - Koch, Thorsten A1 - Kuberek, Monika A1 - Lohrum, Stefan A1 - Pampel, Heinz A1 - Putnings, Markus A1 - Retter, Regina A1 - Rusch, Beate A1 - Schäffler, Hildegard A1 - Söllner, Konstanze A1 - Steffen, Ronald A1 - Wannick, Eike T1 - DeepGreen: Open-Access-Transformation in der Informationsinfrastruktur – Anforderungen und Empfehlungen, Version 1.0 N2 - DeepGreen ist ein Service, der es teilnehmenden institutionellen Open-Access-Repositorien,Open-Access-Fachrepositorien und Forschungsinformationssystemen erleichtert, für sie relevante Verlagspublikationen in zyklischer Abfolge mithilfe von Schnittstellen Open Access zur Verfügung zu stellen. Die entsprechende Bandbreite an Relationen zwischen den Akteuren, diverse lizenzrechtliche Rahmenbedingungen sowie technische Anforderungen gestalten das Thema komplex. Ziel dieser Handreichung ist es, neben all diesen Themen, die begleitend beleuchtet werden, im Besonderen Empfehlungen für die reibungslose Nutzung der Datenübertragung zu liefern. Außerdem werden mithilfe einer vorangestellten Workflow- Evaluierung Unterschiede und Besonderheiten in den Arbeitsschritten bei institutionellen Open-Access-Repositorien und Open-Access-Fachrepositorien aufgezeigt und ebenfalls mit Empfehlungen angereichert. T3 - ZIB-Report - 21-03 KW - Open Access KW - Zweitveröffentlichung KW - Bibliotheken KW - Verlage Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-81503 SN - 1438-0064 ER -