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Die Positionierung des B-FMT der Vibrant Bonebridge kann aufgrund der anatomischen Verhältnisse des Mastoids und der Größe des Aktuators ohne eine vorherige Beurteilung der individuellen Computertomographie (CT) des Felsenbeins problematisch sein. Die Entwicklung eines einfach zu bedienenden Viewers, welcher eine Positionierung des B-FMT im Felsenbeinmodell ermöglicht und hier auf individuelle potenzielle anatomische Konflikte hinweist sowie Lösungsmöglichkeiten anbietet, kann ein hilfreiches Werkzeug zur präoperativen Positionierung sein.
Ziel der Arbeit war die Definition von Anforderungen und die Anfertigung eines Prototyps eines Vibrant-Bonebridge-Viewers.
Auf der Basis einer ZIBAmira-Software-Version und der Inklusion eines B-FMT-Modells unter Erstellung eines Felsenbeinmodells, welches die intuitive Beurteilung von Konflikten ermöglicht, erfolgte die Erstellung des Prototyps eines Vibrant-Bonebridge-Viewers.Ergebnisse. Die Segmentierungszeit der individuellen DICOM-Daten („digital imaging and communications in medicine“) beträgt etwa 5 min. Eine Positionierung im individuellen 3-D-Felsenbeinmodell ermöglicht die quantitative und qualitative Beurteilung von Konflikten (Sinus sigmoideus, mittlere Schädelgrube) und das Aufsuchen einer bevorzugten Position. Das Anheben des B-FMT mittels virtueller Unterlegscheiben kann simuliert werden.
Der erstellte Vibrant-Bonebridge-Viewer ermöglicht verlässlich eine Simulation der B-FMT-Positionierung. Die klinische Anwendbarkeit muss evaluiert werden.
Information about the temporal bone size and variations of anatomical structures are crucial for a safe positioning of the Vibrant Bonebridge B-FMT. A radiological based preoperative planning of the surgical procedure decreases the surgical time and minimizes the risk of complications.
We developed a software tool, which allows a catch up of foreign DICOM data based CT temporal bone scans. The individual CT scan is transmitted into a 3D reconstructed pattern of the temporal bone. In this 3D reconstruction the individually favored position of the B- FMT should be found.
The software allows a determination of a safe B-FMT position by identifying the individual relation of middle fossa, jugular bulb and external auditory canal. Skull thickness and screw length are contained parameters for the surgical planning.
An easy to handle software tool allows a radiologically data based safe and fast surgical positioning of the B-FMT.
Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://osf.io/nqjyw/, https://osf.io/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://github.com/anjany/verse.