ST-V-Net: incorporating shape prior into convolutional neural networks for proximal femur segmentation
| Title: | ST-V-Net: incorporating shape prior into convolutional neural networks for proximal femur segmentation |
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| Authors: | 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 |
| Source: | Complex & Intelligent Systems, Vol 9, Iss 3, Pp 2747-2758 (2021) |
| Publisher Information: | Springer, 2021. |
| Publication Year: | 2021 |
| Collection: | LCC:Electronic computers. Computer science LCC:Information technology |
| Subject Terms: | Quantitative computed tomography, Proximal femur, Segmentation, Deep learning, Convolutional neural networks, Electronic computers. Computer science, QA75.5-76.95, Information technology, T58.5-58.64 |
| More Details: | 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. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2199-4536 2198-6053 |
| Relation: | https://doaj.org/toc/2199-4536; https://doaj.org/toc/2198-6053 |
| DOI: | 10.1007/s40747-021-00427-5 |
| Access URL: | https://doaj.org/article/223ea72c2a434dc7a1954511014d2658 |
| Accession Number: | edsdoj.223ea72c2a434dc7a1954511014d2658 |
| Database: | Directory of Open Access Journals |
| ISSN: | 21994536 21986053 |
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| DOI: | 10.1007/s40747-021-00427-5 |
| Published in: | Complex & Intelligent Systems |
| Language: | English |