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Updated: Aug 1, 2025

Multi-modal Imaging of Angiogenesis in a Nude Rat Model of Breast Cancer Bone Metastasis Using Magnetic Resonance Imaging, Volumetric Computed Tomography and Ultrasound

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

Peng Wan, Haiyan Xue, Chunrui Liu

IEEE Journal of Biomedical and Health Informatics

|April 25, 2023

Summary

This summary is machine-generated.

This study introduces a new AI network (DpRAN) for automatically segmenting lesions in dynamic contrast-enhanced ultrasound (CEUS) images. The DpRAN method accurately identifies lesion characteristics, improving diagnostic capabilities for conditions like thyroid nodules.

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Area of Science:

Medical imaging
Artificial intelligence
Ultrasound technology

Background:

Dynamic contrast-enhanced ultrasound (CEUS) is crucial for real-time microvascular perfusion analysis in lesion detection and characterization.
Accurate lesion segmentation is essential for quantitative and qualitative perfusion analysis in CEUS imaging.

Purpose of the Study:

To propose a novel dynamic perfusion representation and aggregation network (DpRAN) for automatic lesion segmentation in dynamic CEUS imaging.
To address the challenge of modeling enhancement dynamics in various perfusion areas within CEUS data.

Main Methods:

The DpRAN network divides enhancement features into short-range patterns and long-range tendencies.
Introduced perfusion excitation (PE) gate for real-time enhancement representation and cross-attention temporal aggregation (CTA) for global view.
Incorporated an uncertainty estimation strategy to identify critical enhancement points.

Main Results:

The DpRAN method achieved a mean Dice coefficient (DSC) of 0.794 and Intersection over Union (IoU) of 0.676 on CEUS thyroid nodule datasets.
Demonstrated superior performance in capturing distinguished enhancement characteristics for lesion recognition.

Conclusions:

The DpRAN method effectively segments lesions in dynamic CEUS imaging by modeling complex enhancement dynamics.
The proposed approach shows significant potential for enhancing diagnostic accuracy in ultrasound-based lesion characterization.