TY - GEN
T1 - Conditioned Variational Auto-encoder for Detecting Osteoporotic Vertebral Fractures
AU - Husseini, Malek
AU - Sekuboyina, Anjany
AU - Bayat, Amirhossein
AU - Menze, Bjoern H.
AU - Loeffler, Maximilian
AU - Kirschke, Jan S.
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - 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 a reconstructing conditioned-variational auto-encoder architecture and a discriminating 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%.
AB - 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 a reconstructing conditioned-variational auto-encoder architecture and a discriminating 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%.
KW - Fracture detection
KW - Variational Autoencoders
UR - http://www.scopus.com/inward/record.url?scp=85080896810&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-39752-4_3
DO - 10.1007/978-3-030-39752-4_3
M3 - Conference contribution
AN - SCOPUS:85080896810
SN - 9783030397517
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 29
EP - 38
BT - Computational Methods and Clinical Applications for Spine Imaging - 6th International Workshop and Challenge, CSI 2019, Proceedings
A2 - Cai, Yunliang
A2 - Wang, Liansheng
A2 - Audette, Michel
A2 - Zheng, Guoyan
A2 - Li, Shuo
PB - Springer
T2 - 6th International Workshop and Challenge on Computational Methods and Clinical Applications for Spine Imaging, CSI 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019
Y2 - 17 October 2019 through 17 October 2019
ER -