Four-phase CT lesion recognition based on multi-phase information fusion framework and spatiotemporal prediction module

• Published in: Biomedical engineering online Vol. 23; no. 1; pp. 103 - 18
• Main Authors: Qiao, Shaohua, Xue, Mengfan, Zuo, Yan, Zheng, Jiannan, Jiang, Haodong, Zeng, Xiangai, Peng, Dongliang
• Format: Journal Article
• Language: English
• Published: London BioMed Central 21.10.2024; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Accuracy; Algorithms; Analysis; Artificial intelligence; Biomaterials; Biomedical Engineering and Bioengineering; Biomedical Engineering/Biotechnology; Biotechnology; Classification; Computed tomography; Contrast-enhanced CT; CT imaging; Data augmentation; Data integration; Deep learning; Engineering; Health aspects; Hepatic malignancy; Humans; Image enhancement; Image processing; Image Processing, Computer-Assisted - methods; Information processing; Lesions; Liver cancer; Machine learning; Medical diagnosis; Memory; Methods; Modules; Multiphase; Multisensor fusion; Neural networks; Physiology; Predictions; Prevention; Research methodology; Risk factors; Spatial data; Spatial discrimination; Time Factors; Tomography, X-Ray Computed; Tumors; China; Deep learning; Contrast-enhanced CT; Hepatic malignancy; Artificial intelligence; Classification
• ISSN: 1475-925X; 1475-925X
• DOI: 10.1186/s12938-024-01297-x

Multiphase information fusion and spatiotemporal feature modeling play a crucial role in the task of four-phase CT lesion recognition. In this paper, we propose a four-phase CT lesion recognition algorithm based on multiphase information fusion framework and spatiotemporal prediction module. Specifically, the multiphase information fusion framework uses the interactive perception mechanism to realize the channel-spatial information interactive weighting between multiphase features. In the spatiotemporal prediction module, we design a 1D deep residual network to integrate multiphase feature vectors, and use the GRU architecture to model the temporal enhancement information between CT slices. In addition, we employ CT image pseudo-color processing for data augmentation and train the whole network based on a multi-task learning framework. We verify the proposed network on a four-phase CT dataset. The experimental results show that the proposed network can effectively fuse the multi-phase information and model the temporal enhancement information between CT slices, showing excellent performance in lesion recognition.