Dynamic Perfusion Representation and Aggregation Network for Nodule Segmentation Using Contrast-Enhanced US

Dynamic contrast-enhanced ultrasound (CEUS) imaging has been widely applied in lesion detection and characterization, due to its offered real-time observation of microvascular perfusion. Accurate lesion segmentation is of great importance to the quantitative and qualitative perfusion analysis. In this paper, we propose a novel dynamic perfusion representation and aggregation network (DpRAN) for the automatic segmentation of lesions using dynamic CEUS imaging. The core challenge of this work lies in enhancement dynamics modeling of various perfusion areas. Specifically, we divide enhancement features into the two scales: short-range enhancement patterns and long-range evolution tendency. To effectively represent real-time enhancement characteristics and aggregate them in a global view, we introduce the perfusion excitation (PE) gate and cross-attention temporal aggregation (CTA) module, respectively. Different from the common temporal fusion methods, we also introduce an uncertainty estimation strategy to assist the model to locate the critical enhancement point first, in which a relatively distinguished enhancement pattern is displayed. The segmentation performance of our DpRAN method is validated on our collected CEUS datasets of thyroid nodules. We obtain the mean dice coefficient (DSC) and intersection of union (IoU) of 0.794 and 0.676, respectively. Superior performance demonstrates its efficacy to capture distinguished enhancement characteristics for lesion recognition.