- Malek Husseini13,14,
- Anjany Sekuboyina13,14,
- Amirhossein Bayat13,14,
- Bjoern H. Menze13,
- Maximilian Loeffler14 &
- …
- Jan S. Kirschke14
Part of the book series:Lecture Notes in Computer Science ((LNIP,volume 11963))
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Abstract
Detection of osteoporotic vertebral fractures in CT scans is a particularly challenging task that was never sufficiently addressed. This is due to the large variation among healthy vertebrae and the different shapes a fracture could present itself in. In this paper, we combine areconstructing conditioned-variational auto-encoder architecture and adiscriminating multi-layer-perceptron (MLP) to capture these different shapes. We also introduce a vertebrae-specific loss-weighing regime that maximizes the classification yield. Furthermore, we ‘look into’ the learnt network by investigating the saliency maps, traversing the latent space and demonstrating its smoothness. Finally, we report our results on two datasets, including the publicly available xVertSeg dataset achieving an F1 score of 84%.
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Authors and Affiliations
Department of Computer Science, Technical University of Munich, Munich, Germany
Malek Husseini, Anjany Sekuboyina, Amirhossein Bayat & Bjoern H. Menze
Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
Malek Husseini, Anjany Sekuboyina, Amirhossein Bayat, Maximilian Loeffler & Jan S. Kirschke
- Malek Husseini
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- Anjany Sekuboyina
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- Amirhossein Bayat
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- Bjoern H. Menze
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Correspondence toMalek Husseini.
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Worcester Polytechnic Institute, Worcester, MA, USA
Yunliang Cai
Xiamen University, Xiamen, China
Liansheng Wang
Old Dominion University, Norfolk, VA, USA
Michel Audette
Shanghai Jiao Tong University, Shanghai, China
Guoyan Zheng
Western University, London, ON, Canada
Shuo Li
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Husseini, M., Sekuboyina, A., Bayat, A., Menze, B.H., Loeffler, M., Kirschke, J.S. (2020). Conditioned Variational Auto-encoder for Detecting Osteoporotic Vertebral Fractures. In: Cai, Y., Wang, L., Audette, M., Zheng, G., Li, S. (eds) Computational Methods and Clinical Applications for Spine Imaging. CSI 2019. Lecture Notes in Computer Science(), vol 11963. Springer, Cham. https://doi.org/10.1007/978-3-030-39752-4_3
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