Microtomography of the Baltic amber tick Ixodes succineus reveals affinities with the modern Asian disease vector Ixodes ovatus

BACKGROUND: Fossil ticks are extremely rare and Ixodes succineus Weidner, 1964 from Eocene (ca. 44–49 Ma) Baltic amber is one of the oldest examples of a living hard tick genus (Ixodida: Ixodidae). Previous work suggested it was most closely related to the modern and widespread European sheep tick Ixodes ricinus (Linneaus, 1758). RESULTS: Restudy using phase contrast synchrotron x-ray tomography yielded images of exceptional quality. These confirm the fossil’s referral to Ixodes Latreille, 1795, but the characters resolved here suggest instead affinities with the Asian subgenus Partipalpiger Hoogstraal et al., 1973 and its single living (and medically significant) species Ixodes ovatus Neumann, 1899. We redescribe the amber fossil here as Ixodes (Partipalpiger) succineus. CONCLUSIONS: Our data suggest that Ixodes ricinus is unlikely to be directly derived from Weidner’s amber species, but instead reveals that the Partipalpiger lineage was originally more widely distributed across the northern hemisphere. The closeness of Ixodes (P.) succineus to a living vector of a wide range of pathogens offers the potential to correlate its spatial and temporal position (northern Europe, nearly 50 million years ago) with the estimated origination dates of various tick-borne diseases

3D-Rekonstruktion anatomischer Strukturen aus 2D-Röntgenaufnahmen

Two-dimensional (2D) radiographs are widely used for diagnosis, treatment planning, and follow-up in orthopedics. The images depict bone at high contrast to surrounding tissue and capture weight-bearing situations when taken in upright position. Compared to three-dimensional (3D) computed tomography (CT), 2D radiographs further expose the patient to relatively low radiation doses. Projectional radiography does, however, currently not allow for a full 3D assessment of anatomical structures. Measurements on 2D radiographs are inherently limited to the 2D plane only, and it is often difficult for an observer to resolve ambiguities in 3D anatomical shape and pose due to perspective distortion and overlapping structures in a given image. This dissertation addresses the issue of clinical 3D assessment of anatomical shape, position, and spatial relation from 2D X-ray images through computer-aided reconstruction techniques. For this purpose, a fully automated method is proposed to derive the patient-specific 3D shape and pose of bony structures from a single or a few clinical 2D radiographs by means of intensity-based 2D/3D registration. The method generates different variations of 3D volumetric meshes from articulated statistical shape and intensity models (A-SSIMs), with each mesh representing a plausible candidate of the patient-specific shape and bone-interior radiodensity according to a training population of CT datasets. The aim is to find the model instance that matches the depiction in the given 2D radiograph best, as it is assumed to represent a good approximation of the true, patient-specific anatomy. This is achieved by means of an optimization process, in which virtual X-ray images that mimic real X-rays of the anatomical structures of interest are projected from the 3D mesh variations until the similarity between virtual images and the given clinical images is maximized. The individual processing steps of the 2D/3D reconstruction framework are evaluated in detail based on clinical radiographs as well as artificially generated digitally reconstructed radiographs (DRRs) from CTs. Furthermore, the reconstruction method as a whole is evaluated in context of clinical applications in hip-joint replacement and osteosynthesis, showing the benefit of the proposed approach for treatment planning and postoperative follow-up in orthopedics.Die konventionelle Röntgenuntersuchung mittels Projektionsradiographie spielt eine wichtige Rolle in der medizinischen Diagnose, Behandlungsplanung und der Nachuntersuchung von orthopädischen Eingriffen. Röntgenbilder stellen Knochen in hohem Kontrast zu umliegenden Gewebe dar, können in stehender Position unter Last aufgenommen werden, und setzen den Patienten einer relativ geringen Strahlendosis im Vergleich zur Computertomographie (CT) aus. Die Projektionsradiographie erlaubt allerdings keine vollständige Bewertung anatomischer Strukturen im dreidimensionalen (3D-) Raum. Messungen auf Röntgenbilder sind naturgemäß nur auf eine zweidimensionale (2D-) Ebene beschränkt, und es ist selbst für Experten oft schwierig, Mehrdeutigkeiten bezüglich der anatomischen Form und Lage in einer gegebenen Röntgenaufnahme aufzulösen. In dieser Arbeit wird ein vollautomatisches, computergestütztes Verfahren vorgestellt, um die patientenspezifische 3D-Form und Lage von knöchernen Strukturen aus einer oder wenigen klinischen 2D-Röntgenaufnahmen zu rekonstruieren. Die Methode wählt 3D-Repräsentationen der Knochen aus artikulierten, statistischen Form- und Intensitätsmodellen (A-SSIMs), welche die Variation anatomischer Strukturen in Form, Röntgendichte und relativer Lage in einer gegebenen Population von CT-Datensätzen ausdrücken. In einem Optimierungsprozess werden die Modelle an die klinischen Röntgenbilder angepasst, bis ein Ähnlichkeitsmaß zwischen virtuellen Röntgenprojektionen aus den 3D-Repräsentationen und den klinischen Aufnahmen maximiert ist. Die Repräsentation, deren Darstellung in den virtuellen Röntgenbildern am besten den klinischen Röntgenaufnahmen entspricht, stellt dann eine Annäherung an die tatsächliche patientenspezifische 3D-Anatomie dar. Die einzelnen Verarbeitungsschritte des Rekonstruktionsverfahrens werden eingehend anhand klinischer Röntgenbilder und synthetischer Röntgenaufnahmen aus CTs evaluiert. Darüber hinaus wird die Rekonstruktionsmethode als Ganzes im Kontext klinischer Anwendungen wie dem Hüftgelenkersatz und der Osteosynthese ausgewertet. Die Ergebnisse zeigen den potentiellen Nutzen der intensitätsbasierten Rekonstruktionsmethode für eine Vielzahl von Anwendungen in der präoperativen Planung und Nachuntersuchung

AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design

The aim of this paper is to provide a comprehensive overview of the MICCAI 2020 AutoImplant Challenge. The approaches and publications submitted and accepted within the challenge will be summarized and reported, highlighting common algorithmic trends and algorithmic diversity. Furthermore, the evaluation results will be presented, compared and discussed in regard to the challenge aim: seeking for low cost, fast and fully automated solutions for cranial implant design. Based on feedback from collaborating neurosurgeons, this paper concludes by stating open issues and post-challenge requirements for intra-operative use. The codes can be found at https://github.com/Jianningli/tmi

AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design

The aim of this paper is to provide a comprehensive overview of the MICCAI 2020 AutoImplant Challenge. The approaches and publications submitted and accepted within the challenge will be summarized and reported, highlighting common algorithmic trends and algorithmic diversity. Furthermore, the evaluation results will be presented, compared and discussed in regard to the challenge aim: seeking for low cost, fast and fully automated solutions for cranial implant design. Based on feedback from collaborating neurosurgeons, this paper concludes by stating open issues and post-challenge requirements for intra-operative use. The codes can be found at https://github.com/Jianningli/tmi

AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design

The aim of this paper is to provide a comprehensive overview of the MICCAI 2020 AutoImplant Challenge. The approaches and publications submitted and accepted within the challenge will be summarized and reported, highlighting common algorithmic trends and algorithmic diversity. Furthermore, the evaluation results will be presented, compared and discussed in regard to the challenge aim: seeking for low cost, fast and fully automated solutions for cranial implant design. Based on feedback from collaborating neurosurgeons, this paper concludes by stating open issues and post-challenge requirements for intra-operative use.Fil: Li, Jianning. Technische Universitat Graz; AustriaFil: Pimentel, Pedro. No especifíca;Fil: Szengel, Angelika. No especifíca;Fil: Ehlke, Moritz. No especifíca;Fil: Lamecker, Hans. No especifíca;Fil: Zachow, Stefan. No especifíca;Fil: Estacio, Laura. Universidad Católica San Pablo; PerúFil: Doenitz, Christian. No especifíca;Fil: Ramm, Heiko. No especifíca;Fil: Shi, Haochen. Shanghai Jiao Tong University; ChinaFil: Chen, Xiaojun. Shanghai Jiao Tong University; ChinaFil: Matzkin, Victor Franco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Newcombe, Virginia. University of Cambridge; Estados UnidosFil: Ferrante, Enzo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Jin, Yuan. Technische Universitat Graz; AustriaFil: Ellis, David G.. No especifíca;Fil: Aizenberg, Michele R.. University of Nebraska; Estados UnidosFil: Kodym, Oldrich. No especifíca;Fil: Spanel, Michal. No especifíca;Fil: Herout, Adam. No especifíca;Fil: Mainprize, James G.. Sunnybrook Health Sciences Centre; CanadáFil: Fishman, Zachary. Sunnybrook Health Sciences Centre; CanadáFil: Hardisty, Michael R.. Sunnybrook Health Sciences Centre; CanadáFil: Bayat, Amirhossein. No especifíca;Fil: Shit, Suprosanna. No especifíca;Fil: Wang, Bomin. Shandong University; ChinaFil: Liu, Zhi. Shandong University; ChinaFil: Eder, Matthias. Technische Universitat Graz; AustriaFil: Pepe, Antonio. Technische Universitat Graz; AustriaFil: Gsaxner, Christina. Technische Universitat Graz; AustriaFil: Alves, Victor. Universidade do Minho; PortugalFil: Zefferer, Ulrike. Medizinische Universität Graz; AustriaFil: Von Campe, Gord. Medizinische Universität Graz; AustriaFil: Pistracher, Karin. Medizinische Universität Graz; AustriaFil: Schafer, Ute. Medizinische Universität Graz; AustriaFil: Schmalstieg, Dieter. Technische Universitat Graz; AustriaFil: Menze, Bjoern H.. No especifíca;Fil: Glocker, Ben. Imperial College London; Reino UnidoFil: Egger, Jan. Computer Algorithms For Medicine Laboratory; Austri

AutoImplant 2020 - First MICCAI Challenge on Automatic Cranial Implant Design

The aim of this paper is to provide a comprehensive overview of the MICCAI 2020 AutoImplant Challenge. The approaches and publications submitted and accepted within the challenge will be summarized and reported, highlighting common algorithmic trends and algorithmic diversity. Furthermore, the evaluation results will be presented, compared and discussed in regard to the challenge aim: seeking for low cost, fast and fully automated solutions for cranial implant design. Based on feedback from collaborating neurosurgeons, this paper concludes by stating open issues and post-challenge requirements for intra-operative use. The codes can be found at https://github.com/Jianningli/tmi.FWF - Austrian Science Fund(KLI 678-B31