Remove Appearance Shift for Ultrasound Image Segmentation via Fast and Universal Style Transfer

TL;DR

This paper introduces a universal style transfer framework to mitigate appearance shifts in ultrasound images, significantly enhancing the robustness and real-time performance of deep neural networks in medical image segmentation.

Contribution

It pioneers the application of universal style transfer to ultrasound images, enabling arbitrary style-content transfer without losing structural details, and introduces an efficient style image selection strategy.

Findings

Outperforms state-of-the-art methods in robustness against appearance shifts.

Achieves real-time processing suitable for clinical US scanning.

Demonstrates superior segmentation accuracy on large US datasets.

Abstract

Deep Neural Networks (DNNs) suffer from the performance degradation when image appearance shift occurs, especially in ultrasound (US) image segmentation. In this paper, we propose a novel and intuitive framework to remove the appearance shift, and hence improve the generalization ability of DNNs. Our work has three highlights. First, we follow the spirit of universal style transfer to remove appearance shifts, which was not explored before for US images. Without sacrificing image structure details, it enables the arbitrary style-content transfer. Second, accelerated with Adaptive Instance Normalization block, our framework achieved real-time speed required in the clinical US scanning. Third, an efficient and effective style image selection strategy is proposed to ensure the target-style US image and testing content US image properly match each other. Experiments on two large US datasets demonstrate that our methods are superior to state-of-the-art methods on making DNNs robust against various appearance shifts.