ST-V-Net: incorporating shape prior into convolutional neural networks for proximal femur segmentation

Chen Zhao, Joyce H. Keyak, Jinshan Tang, Tadashi S. Kaneko, Sundeep Khosla, Shreyasee Amin, Elizabeth J. Atkinson, Lan Juan Zhao, Michael J. Serou, Chaoyang Zhang, Hui Shen, Hong Wen Deng, Weihua Zhou

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
JournalComplex and Intelligent Systems
DOIs
StateAccepted/In press - 2021

Keywords

  • Convolutional neural networks
  • Deep learning
  • Proximal femur
  • Quantitative computed tomography
  • Segmentation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems
  • Engineering (miscellaneous)
  • Computational Mathematics

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