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

• Published in: Complex & intelligent systems Vol. 9; no. 3; pp. 2747 - 2758
• Main Authors: Zhao, Chen, Keyak, Joyce H., Tang, Jinshan, Kaneko, Tadashi S., Khosla, Sundeep, Amin, Shreyasee, Atkinson, Elizabeth J., Zhao, Lan-Juan, Serou, Michael J., Zhang, Chaoyang, Shen, Hui, Deng, Hong-Wen, Zhou, Weihua
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.06.2023; Springer Nature B.V; Springer
• Subjects: Artificial neural networks; Complexity; Computational Intelligence; Computed tomography; Constraint modelling; Convolutional neural networks; Data Structures and Information Theory; Deep learning; Engineering; Femur; Image segmentation; Metric space; Original; Original Article; Performance evaluation; Proximal femur; Quantitative analysis; Quantitative computed tomography; Segmentation; Training; Deep learning; Quantitative computed tomography; Segmentation; Convolutional neural networks; Proximal femur
• ISSN: 2199-4536; 2198-6053; 2198-6053
• DOI: 10.1007/s40747-021-00427-5

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.