Abstract
Osteoporotic vertebral fractures have a severe impact on patients’ overall well-being but are severely under-diagnosed. These fractures present themselves at various levels of severity measured using the Genant’s grading scale. Insufficient annotated datasets, severe data-imbalance, and minor difference in appearances between fractured and healthy vertebrae make naive classification approaches result in poor discriminatory performance. Addressing this, we propose a representation learning-inspired approach for automated vertebral fracture detection, aimed at learning latent representations efficient for fracture detection. Building on state-of-art metric losses, we present a novel Grading Loss for learning representations that respect Genant’s fracture grading scheme. On a publicly available spine dataset, the proposed loss function achieves a fracture detection F1 score of 81.5%, a 10% increase over a naive classification baseline.
M. Husseini and A. Sekuboyina—Shared first authors.
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Acknowledgements
This work is supported by DIFUTURE, funded by the German Federal Ministry of Education and Research under (01ZZ1603[A-D]) and (01ZZ1804[A-I]).
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Husseini, M., Sekuboyina, A., Loeffler, M., Navarro, F., Menze, B.H., Kirschke, J.S. (2020). Grading Loss: A Fracture Grade-Based Metric Loss for Vertebral Fracture Detection. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12266. Springer, Cham. https://doi.org/10.1007/978-3-030-59725-2_71
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