@misc{KnoetelSeidelProhaskaetal., 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}, 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., 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}, series = {PLOS ONE}, journal = {PLOS ONE}, doi = {10.1371/journal.pone.0188018}, 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} } @misc{KnoetelSeidelZaslanskyetal., 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}, 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{BertelmannKochCeynowaetal., author = {Bertelmann, Roland and Koch, Thorsten and Ceynowa, Klaus and S{\"o}llner, Konstanze and Christof, J{\"u}rgen and Rusch, Beate and Sch{\"a}ffler, Hildegard and Putnings, Markus and Pampel, Heinz and Kuberek, Monika and Boltze, Julia and Lohrum, Stefan and Retter, Regina and H{\"o}llerl, Annika and Faensen, Katja and Steffen, Ronald and Gross, Matthias and Hoffmann, Cornelia and Haoua, Marsa}, title = {DeepGreen: Etablierung und Weiterentwicklung rechtssicherer Workflows zur effizienten Umsetzung von Open-Access-Komponenten in Lizenzvereinbarungen f{\"u}r wissenschaftliche Publikationen - Abschlussbericht}, issn = {1438-0064}, doi = {10.12752/8542}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-85420}, abstract = {DeepGreen wurde vom 01.08.2018 bis zum 30.06.2021 in einer zweiten Projektphase von der Deutschen Forschungsgemeinschaft (DFG) gef{\"o}rdert. DeepGreen unterst{\"u}tzt Bibliotheken als Dienstleister f{\"u}r Hochschulen, außeruniversit{\"a}re Forschungseinrichtungen und die dort t{\"a}tigen Wissenschaftler:innen dabei, Publikationen auf Open-Access-Repositorien frei zug{\"a}nglich zu machen und f{\"o}rdert das Zusammenspiel von wissenschaftlichen Einrichtungen und Verlagen. An der zweiten Projektphase waren der Kooperative Bibliotheksverbund Berlin-Brandenburg, die Bayerische Staatsbibliothek, der Bibliotheksverbund Bayern, die Universit{\"a}tsbibliotheken der Friedrich-Alexander-Universit{\"a}t Erlangen-N{\"u}rnberg und der Technischen Universit{\"a}t Berlin und das Helmholtz Open Science Office beteiligt. In dem Projekt wurde erfolgreich eine technische und organisatorische L{\"o}sung 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-gef{\"o}rderte Projektlaufzeit ist DeepGreen in einen zweij{\"a}hrigen Pilotbetrieb {\"u}bergegangen. Ziel des Pilotbetriebs ist es, den {\"U}bergang in einen bundesweiten Real-Betrieb vorzubereiten.}, language = {de} } @misc{BertelmannBoltzeCeynowaetal., author = {Bertelmann, Roland and Boltze, Julia and Ceynowa, Klaus and Christof, J{\"u}rgen and Faensen, Katja and Groß, Matthias and Hoffmann, Cornelia and Koch, Thorsten and Kuberek, Monika and Lohrum, Stefan and Pampel, Heinz and Putnings, Markus and Retter, Regina and Rusch, Beate and Sch{\"a}ffler, Hildegard and S{\"o}llner, Konstanze and Steffen, Ronald and Wannick, Eike}, title = {DeepGreen: Open-Access-Transformation in der Informationsinfrastruktur - Anforderungen und Empfehlungen, Version 1.0}, issn = {1438-0064}, doi = {10.12752/8150}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-81503}, abstract = {DeepGreen ist ein Service, der es teilnehmenden institutionellen Open-Access-Repositorien,Open-Access-Fachrepositorien und Forschungsinformationssystemen erleichtert, f{\"u}r sie relevante Verlagspublikationen in zyklischer Abfolge mithilfe von Schnittstellen Open Access zur Verf{\"u}gung 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 f{\"u}r die reibungslose Nutzung der Daten{\"u}bertragung 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.}, language = {de} }