%0 Conference Paper %1 kottke2015data %A Kottke, Daniel %A Gulamhussene, Gino %A Tönnies, Klaus %B Workshop über Bildverarbeitung für die Medizin (BVM) %D 2015 %I Springer %K curved detection imaging mri not_ies planar reconstruction registration regression spine vertebra %P 5-10 %R 10.1007/978-3-662-46224-9_3 %T Data-Driven Spine Detection for Multi-Sequence MRI %X Epidemiology studies on vertebra's shape and appearance require big databases of medical images and image processing methods, that are robust against deformation and noise. This work presents a solution of the first step: the vertebrae detection. We propose a method that automatically detects the central spinal curve with 3D data-driven methods in multi-sequence magnetic resonance images (MRl). Additionally, we use simple edge operations for vertebra border detection that can be used for a statistical evaluation with help of some fast user interaction. Our automatic vertebrae detection algorithm fits a polynomial curve through the spinal canal, that afterwards is shifted towards the vertebra centers. An edge operator gives a first approximation of the vertebra borders, that can be evaluated and corrected by some user interaction within 12 seconds. We show, that our algorithm automatically detects more than 90\% of all spines correctly, and present a preliminary analysis of vertebrae sizes. %@ 978-3-662-46223-2

@inproceedings{kottke2015data, abstract = {Epidemiology studies on vertebra's shape and appearance require big databases of medical images and image processing methods, that are robust against deformation and noise. This work presents a solution of the first step: the vertebrae detection. We propose a method that automatically detects the central spinal curve with 3D data-driven methods in multi-sequence magnetic resonance images (MRl). Additionally, we use simple edge operations for vertebra border detection that can be used for a statistical evaluation with help of some fast user interaction. Our automatic vertebrae detection algorithm fits a polynomial curve through the spinal canal, that afterwards is shifted towards the vertebra centers. An edge operator gives a first approximation of the vertebra borders, that can be evaluated and corrected by some user interaction within 12 seconds. We show, that our algorithm automatically detects more than 90{\%} of all spines correctly, and present a preliminary analysis of vertebrae sizes.}, added-at = {2022-01-07T10:37:59.000+0100}, author = {Kottke, Daniel and Gulamhussene, Gino and Tönnies, Klaus}, biburl = {https://www.bibsonomy.org/bibtex/21a5cba0f4ffd96e029349dcd762bb778/ies}, booktitle = {Workshop über Bildverarbeitung für die Medizin (BVM)}, doi = {10.1007/978-3-662-46224-9_3}, interhash = {daf2de716a544810744378298b345d06}, intrahash = {1a5cba0f4ffd96e029349dcd762bb778}, isbn = {978-3-662-46223-2}, keywords = {curved detection imaging mri not_ies planar reconstruction registration regression spine vertebra}, pages = {5-10}, publisher = {Springer}, timestamp = {2022-01-07T10:37:59.000+0100}, title = {Data-Driven Spine Detection for Multi-Sequence MRI}, year = 2015 }