Refine
Document Type
- Article (7)
- ZIB-Report (3)
- In Proceedings (2)
- Poster (2)
Is part of the Bibliography
- no (14)
Keywords
- image registration (2)
- 3D reconstruction (1)
- GPU acceleration (1)
- acetabular orientation (1)
- articulated shape and intensity models (1)
- bone anchored hearing implant (1)
- digitally reconstructed radiographs (1)
- mesh deformation (1)
- segmentation (1)
- statistical shape and intensity models (1)
- surgery planning (1)
- visualization (1)
- volume rendering (1)
Institute
- Therapy Planning (14) (remove)
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.
We present an automated method for extrapolating missing
regions in label data of the skull in an anatomically plausible manner. The ultimate goal is to design patient-specic cranial implants for correcting large, arbitrarily shaped defects of the skull that can, for example, result from trauma of the head. Our approach utilizes a 3D statistical shape model (SSM) of the skull and a 2D generative adversarial network (GAN) that is trained in an unsupervised fashion from samples of healthy patients alone. By tting the SSM to given input labels containing the skull defect, a First approximation of the healthy state of the patient is obtained. The GAN is then applied to further correct and smooth the output of the SSM in an anatomically plausible manner. Finally, the defect region is extracted using morphological operations and subtraction between the extrapolated healthy state of the patient and the defective input labels. The method is trained and evaluated based on data from the MICCAI 2020 AutoImplant challenge. It produces state-of-the art results on regularly
shaped cut-outs that were present in the training and testing data of the challenge. Furthermore, due to unsupervised nature of the approach, the method generalizes well to previously unseen defects of varying shapes that were only present in the hidden test dataset.
This study’s objective was the generation of a standardized geometry of the healthy nasal cavity.
An average geometry of the healthy nasal cavity was generated using a statistical shape model based on 25 symptom-free subjects. Airflow within the average geometry and these geometries was calculated using fluid simulations. Integral measures of the nasal resistance, wall shear stresses (WSS) and velocities were calculated as well as cross-sectional areas (CSA). Furthermore, individual WSS and static pressure distributions were mapped onto the average geometry.
The average geometry featured an overall more regular shape that resulted in less resistance, reduced wall shear stresses and velocities compared to the median of the 25 geometries. Spatial distributions of WSS and pressure of average geometry agreed well compared to the average distributions of all individual geometries. The minimal CSA of the average geometry was larger than the median of all individual geometries (83.4 vs. 74.7 mm²).
The airflow observed within the average geometry of the healthy nasal cavity did not equal the average airflow of the individual geometries. While differences observed for integral measures were notable, the calculated values for the average geometry lay within the distributions of the individual parameters. Spatially resolved parameters differed less prominently.
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.