Automated segmentation of vertebral cortex with 3D U-Net-based deep convolutional neural network - Li, Yang; Yao, Qianqian; Yu, Haitao; Xie, Xiaofeng; Shi, Zeren; Li, Shanshan; Qiu, Hui; Li, Changqin; Qin, Jian (Frontiers in Bioengineering and Biotechnology, 2022)
Abstract Objectives: We developed a 3D U-Net-based deep convolutional neural network for the automatic segmentation of vertebral cortex. The purpose of this study was to evaluate the accuracy of the 3D U-Net deep learning model. Methods: 418 3D images were divided into training, validation and test sets according to 7:2:1,and combined with CT scan images using deep learning.Ten fold cross-validation was utilized in the training and validation sets. Through experiments comparing 3D U-Net, Res U-Net, Ki U-Net and Seg Net, the 3D U-Net deep network was finally selected as the segmentation method. The network included an encoding part and a decoding part. The encoding part was used to analyze the entire image and perform feature extraction and analysis. The decoding part generated a segmented block image. The segmentation performance and inference speed of 3D U-Net and the other three deep learning algorithms were evaluated using DSC and FPS. Results: The DSC of 3D U-Net is better than the other three strategies, reaching 0.7139 on the validation set and 0.7038 on the test set, indicating promising automated segmentation results. Conclusion: Cortical bone can be effectively segmented based on 3D U-net.