Document Type
Article
Publication Date
6-16-2021
Department
Department of Applied Computing; Health Research Institute; Institute of Computing and Cybersystems
Abstract
We aim to develop a deep-learning-based method for automatic proximal femur segmentation in quantitative computed tomography (QCT) images. We proposed a spatial transformation V-Net (ST-V-Net), which contains a V-Net and a spatial transform network (STN) to extract the proximal femur from QCT images. The STN incorporates a shape prior into the segmentation network as a constraint and guidance for model training, which improves model performance and accelerates model convergence. Meanwhile, a multi-stage training strategy is adopted to fine-tune the weights of the ST-V-Net. We performed experiments using a QCT dataset which included 397 QCT subjects. During the experiments for the entire cohort and then for male and female subjects separately, 90% of the subjects were used in ten-fold stratified cross-validation for training and the rest of the subjects were used to evaluate the performance of models. In the entire cohort, the proposed model achieved a Dice similarity coefficient (DSC) of 0.9888, a sensitivity of 0.9966 and a specificity of 0.9988. Compared with V-Net, the Hausdorff distance was reduced from 9.144 to 5.917 mm, and the average surface distance was reduced from 0.012 to 0.009 mm using the proposed ST-V-Net. Quantitative evaluation demonstrated excellent performance of the proposed ST-V-Net for automatic proximal femur segmentation in QCT images. In addition, the proposed ST-V-Net sheds light on incorporating shape prior to segmentation to further improve the model performance.
Publication Title
Complex & Intelligent Systems
Recommended Citation
Zhao, C.,
Keyak, J. H.,
Tang, J.,
Kaneko, T. S.,
Khosla, S.,
Zhou, W.,
&
et. al.
(2021).
ST-V-Net: incorporating shape prior into convolutional neural networks for proximal femur segmentation.
Complex & Intelligent Systems.
http://doi.org/10.1007/s40747-021-00427-5
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/15649
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF
Publisher's Statement
© 2021. Authors. Publisher’s version of record: https://doi.org/10.1007/s40747-021-00427-5