TY - GEN
T1 - Sub-Axial Vertebral Column Fracture CT Image Synthesis by Progressive Growing Generative Adversarial Networks (PGGANs)
AU - Sindhura, Dn
AU - Pai, Radhika M.
AU - Bhat, Shyamasunder N.
AU - Pai, Mm Manohara
N1 - Funding Information:
Our sincere thanks to Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal, India.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Orthopaedicians need the assistance of the Deep Learning (DL) model for easy Vertebral Column Fracture Type identification. Deep Learning models require large datasets. Due to the non-availability of large annotated data sets, the DL model needs intensive data augmentation methods. In this proposed research work, Progressive Growing Generative Adversarial Networks (PGGANs) are used to generate synthetic Vertebral Column Fracture (VCF) CT images. The synthetic CT images of VCF generated by PGGANs are high resolution, realistic yet wholly different from the real images. The PGGANs is a multi-stage generative model that generates 512 X 512 CT images that increases the accuracy of the VCF Type identification system. A total of375 vertebral column CT images were utilized for training the model, which were collected from the Spine Clinic, Orthopaedics Department, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal. Among 375 images, 275 Chance fractures and 100 posterior tension band disruption fracture images were present. To analyse the effect of PGGAN augmentation on VCF type identification, lately VGG16 pre-trained model is implemented. The VGG16 model with PGGAN augmentation got an accuracy of 87.01%, which is more when compared to the model without augmentation. In conclusion, PGGAN generated VCF images are realistic and can be used for data augmentation without privacy restrictions and in VCF type identification DL models for increased performance.
AB - Orthopaedicians need the assistance of the Deep Learning (DL) model for easy Vertebral Column Fracture Type identification. Deep Learning models require large datasets. Due to the non-availability of large annotated data sets, the DL model needs intensive data augmentation methods. In this proposed research work, Progressive Growing Generative Adversarial Networks (PGGANs) are used to generate synthetic Vertebral Column Fracture (VCF) CT images. The synthetic CT images of VCF generated by PGGANs are high resolution, realistic yet wholly different from the real images. The PGGANs is a multi-stage generative model that generates 512 X 512 CT images that increases the accuracy of the VCF Type identification system. A total of375 vertebral column CT images were utilized for training the model, which were collected from the Spine Clinic, Orthopaedics Department, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal. Among 375 images, 275 Chance fractures and 100 posterior tension band disruption fracture images were present. To analyse the effect of PGGAN augmentation on VCF type identification, lately VGG16 pre-trained model is implemented. The VGG16 model with PGGAN augmentation got an accuracy of 87.01%, which is more when compared to the model without augmentation. In conclusion, PGGAN generated VCF images are realistic and can be used for data augmentation without privacy restrictions and in VCF type identification DL models for increased performance.
UR - http://www.scopus.com/inward/record.url?scp=85145355233&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85145355233&partnerID=8YFLogxK
U2 - 10.1109/DISCOVER55800.2022.9974676
DO - 10.1109/DISCOVER55800.2022.9974676
M3 - Conference contribution
AN - SCOPUS:85145355233
T3 - 2022 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022 - Proceedings
SP - 311
EP - 315
BT - 2022 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022
Y2 - 14 October 2022 through 15 October 2022
ER -