Objective: Microvascular perfusion can be observed in real time with contrast-enhanced ultrasound (CEUS), which is a novel ultrasound technology for visualizing the dynamic patterns of parenchymal perfusion. Automatic lesion segmentation and differential diagnosis of malignant and benign based on CEUS are crucial but challenging tasks for computer-aided diagnosis of thyroid nodule.
Methods: To tackle these two formidable challenges concurrently, we provide Trans-CEUS, a spatial-temporal transformer-based CEUS analysis model to finish the joint learning of these two challenging tasks. Specifically, the dynamic swin-transformer encoder and multi-level feature collaborative learning are combined into U-net for achieving accurate segmentation of lesions with ambiguous boundary from CEUS. In addition, variant transformer-based global spatial-temporal fusion is proposed to obtain long-distance enhancement perfusion of dynamic CEUS for promoting differential diagnosis.
Results: Empirical results of clinical data showed that our Trans-CEUS model achieved not only a good lesion segmentation result with a high Dice similarity coefficient of 82.41%, but also superior diagnostic accuracy of 86.59%. Conclusion & significance: This research is novel since it is the first to incorporate the transformer into CEUS analysis, and it shows promising results on dynamic CEUS datasets for both segmentation and diagnosis tasks of the thyroid nodule.