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.
%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
}