Semi-XctNet: Volumetric images reconstruction network from a single projection image via semi-supervised learning. (March 2023)

• Record Type: Journal Article
• Authors: Tan, Zhiqiang; Li, Shibo; Hu, Ying; Tao, Huiren; Zhang, Lihai
• Is Part Of: Computers in biology and medicine. Volume 155(2023)
• Publication Date: 2023-03
• Subjects: 3D Reconstruction -- Semi-supervised learning -- Data augmentation -- X-ray -- Volumetric images; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2023.106663
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Deep learning networks have achieved remarkable progress in various tasks of medical imaging. Most of the recent success in computer vision highly depend on large amounts of carefully annotated data, whereas labelling is arduous, time-consuming and in need of expertise. In this paper, a semi-supervised learning method, Semi-XctNet, is proposed for volumetric images reconstruction from a single X-ray image. In our framework, the effect of regularization on pixel-level prediction is enhanced by introducing a transformation consistent strategy into the model. Furthermore, a multi-stage training strategy is designed to ameliorate the generalization performance of the teacher network. An assistant module is also introduced to improve the pixel quality of pseudo-labels, thereby further improving the reconstruction accuracy of the semi-supervised model. The semi-supervised method proposed in this paper has been extensively validated on the LIDC-IDRI lung cancer detection public data set. Quantitative results show that SSIM (structural similarity measurement) and PSNR (peak signal noise ratio) are 0.8384 and 28.7344 respectively. Compared with the state-of-the-arts, Semi-XctNet exhibits excellent reconstruction performance, thus demonstrating the effectiveness of our method on the task of volumetric images reconstruction network from a single X-ray image. Highlights: Volumetric CT images are reconstructed from a single X-ray image based on semi supervised learning method. A multi-stage training strategy is constructed to train the semi-supervised learning model. An assistant module is proposed to improve the accuracy of pseudo-labels.