Dataset Information

Automated segmentation of vertebral cortex with 3D U-Net-based deep convolutional neural network.

ABSTRACT: Objectives: We developed a 3D U-Net-based deep convolutional neural network for the automatic segmentation of the vertebral cortex. The purpose of this study was to evaluate the accuracy of the 3D U-Net deep learning model. Methods: In this study, a fully automated vertebral cortical segmentation method with 3D U-Net was developed, and ten-fold cross-validation was employed. Through data augmentation, we obtained 1,672 3D images of chest CT scans. Segmentation was performed using a conventional image processing method and manually corrected by a senior radiologist to create the gold standard. To compare the segmentation performance, 3D U-Net, Res U-Net, Ki U-Net, and Seg Net were used to segment the vertebral cortex in CT images. The segmentation performance of 3D U-Net and the other three deep learning algorithms was evaluated using DSC, mIoU, MPA, and FPS. Results: The DSC, mIoU, and MPA of 3D U-Net are better than the other three strategies, reaching 0.71 ± 0.03, 0.74 ± 0.08, and 0.83 ± 0.02, respectively, indicating promising automated segmentation results. The FPS is slightly lower than that of Seg Net (23.09 ± 1.26 vs. 30.42 ± 3.57). Conclusion: Cortical bone can be effectively segmented based on 3D U-net.

SUBMITTER: Li Y

PROVIDER: S-EPMC9626964 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Automated segmentation of vertebral cortex with 3D U-Net-based deep convolutional neural network.

Li Yang Y Yao Qianqian Q Yu Haitao H Xie Xiaofeng X Shi Zeren Z Li Shanshan S Qiu Hui H Li Changqin C Qin Jian J

Frontiers in bioengineering and biotechnology 20221019

<b>Objectives:</b> We developed a 3D U-Net-based deep convolutional neural network for the automatic segmentation of the vertebral cortex. The purpose of this study was to evaluate the accuracy of the 3D U-Net deep learning model. <b>Methods:</b> In this study, a fully automated vertebral cortical segmentation method with 3D U-Net was developed, and ten-fold cross-validation was employed. Through data augmentation, we obtained 1,672 3D images of chest CT scans. Segmentation was performed using a ...[more]

PMID: 36338129

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Dataset Information

Automated segmentation of vertebral cortex with 3D U-Net-based deep convolutional neural network.

Publications

Automated segmentation of vertebral cortex with 3D U-Net-based deep convolutional neural network.

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