Papers › Spatio-temporal collaborative multiple-stream transformer network for liver lesion...
Spatio-temporal collaborative multiple-stream transformer network for liver lesion classification on multiple-sequence magnetic resonance imaging
Shuangping Huang, Zinan Hong, Bianzhe Wu, Jinglin Liang, Qinghua Huang
Accurate identification of focal liver lesions is essential for determining the appropriate therapeutic approach in clinical practice. Magnetic resonance imaging (MRI) is a valuable technology for precise classification, revealing diverse physical and biological details of lesions. However, due to the wide variety and morphological variability of the lesions, unsystematic mixing analysis of multiple-sequences MRI may cause aliasing of lesion information, obscuring the inter-tissue relationships between various imaging sequences and impeding a comprehensive diagnosis. In this paper, we proposed a Spatio-Temporal Collaborative Multiple-Stream Transformer Network that simultaneously considers spatial contrast and temporal variations to obtain detailed information on anatomical structures and tissue dynamics, effectively organizing and utilizing multiple-sequence MRI for analysis. Specifically, multiple-sequence MRI is first grouped into multiple streams based on MRI diagnostic characteristics. To reduce the interference of redundancy across multiple streams, we design a bottleneck bridge structure for spatial information aggregation. Additionally, we adopt a bidirectional Long Short-Term Memory to simulate radiologists observing the vascular morphology and hemodynamics in lesion sites from contrast-enhanced sequences. Experiments conducted on public MRI dataset, which includes seven categories of focal liver lesions from 498 patients, demonstrate that our framework achieves state-of-the-art performance, with an accuracy of 85.6%, a precision of 87.4%, a recall of 84.2%, an F1-score of 85.3%, and an Area Under the Curve of 97.1%. The experimental results indicate that the network performs well in predicting focal liver lesions, advancing the application of precision medicine.
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