Zusammenfassung
In order to facilitate early diagnosis and prevention of osteoporosis and degenerative diseases of the spine, automated opportunistic screening in routine 3D-CT scans can be implemented to assist radiologists in clinical practice. The resource limited clinical setting demands for solutions that emphasise accuracy and robustness while oftentimes being limited by computational resources. The VerSe19 and ’20 challenges aim at addressing the task of spine CT analysis but most proposed methods require multiple-stages and are computationally complex.
Chapter PDF
Similar content being viewed by others
References
Hempe H, Yilmaz EB, Meyer C, Heinrich MP. Opportunistic CT screening for degenerative deformities and osteoporotic fractures with 3D DeepLab. Medical Imaging 2022: Image Processing. SPIE, 2022.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
About this paper
Cite this paper
Hempe, H., Heinrich, M.P. (2022). Abstract: Light-weight Semantic Segmentation and Labelling of Vertebrae in 3D-CT Scans. In: Maier-Hein, K., Deserno, T.M., Handels, H., Maier, A., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2022. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-36932-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-658-36932-3_4
Published:
Publisher Name: Springer Vieweg, Wiesbaden
Print ISBN: 978-3-658-36931-6
Online ISBN: 978-3-658-36932-3
eBook Packages: Computer Science and Engineering (German Language)