TY - GEN
T1 - Automatic Vertebrae Segmentation in MR Volumes A Comparison of Different Deep Learning-based Approaches
AU - Xhelili, Orgest
AU - Gafencu, Miruna
AU - De Benetti, Francesca
AU - Navab, Nassir
AU - Wendler, Thomas
N1 - Publisher Copyright:
© 2023 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature.
PY - 2023
Y1 - 2023
N2 - Vertebrae segmentation is important in several clinical settings involving spine pathologies. In recent years, deep learning-based techniques have become popular in segmenting the spine in computed tomography (CT) volumes. However, few options have been tested for magnetic resonance (MR) imaging segmentation. In this paper, we provide a comparison of three deep learning methods tackling the automatic vertebrae segmentation in MR volumes. We selected three methods that were already established in the segmentation of CT images: 3D UNet as our baseline, an iterative binary segmentation approach, and a multi-stage segmentation approach. Our experiments achieved a mean Dice score of 88.1% and demonstrate that CT segmentation methods are easily transferable to MR segmentation.
AB - Vertebrae segmentation is important in several clinical settings involving spine pathologies. In recent years, deep learning-based techniques have become popular in segmenting the spine in computed tomography (CT) volumes. However, few options have been tested for magnetic resonance (MR) imaging segmentation. In this paper, we provide a comparison of three deep learning methods tackling the automatic vertebrae segmentation in MR volumes. We selected three methods that were already established in the segmentation of CT images: 3D UNet as our baseline, an iterative binary segmentation approach, and a multi-stage segmentation approach. Our experiments achieved a mean Dice score of 88.1% and demonstrate that CT segmentation methods are easily transferable to MR segmentation.
UR - https://www.scopus.com/pages/publications/85164951000
U2 - 10.1007/978-3-658-41657-7_9
DO - 10.1007/978-3-658-41657-7_9
M3 - Conference contribution
AN - SCOPUS:85164951000
SN - 9783658416560
T3 - Informatik aktuell
SP - 19
EP - 24
BT - Bildverarbeitung für die Medizin 2023 Proceedings, German Workshop on Medical Image Computing, Braunschweig
A2 - Deserno, Thomas M.
A2 - Handels, Heinz
A2 - Maier, Andreas
A2 - Maier-Hein, Klaus
A2 - Palm, Christoph
A2 - Tolxdorff, Thomas
PB - Springer Science and Business Media Deutschland GmbH
T2 - Bildverarbeitung für die Medizin Workshop, BVM 2023
Y2 - 2 July 2023 through 4 July 2023
ER -