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

arXiv: https://arxiv.org/abs/2002.05844 · HTML: https://mlsanity.com/paper/2002.05844/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2020.

## Built on by (most cited first, 8 of 8)

- [Wave-SAN: Wavelet based Style Augmentation Network for Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2203.07656/) (2022)
- [Generalize Ultrasound Image Segmentation via Instant and Plug & Play Style Transfer](https://mlsanity.com/paper/2101.03711/) (2021)
- [Style Curriculum Learning for Robust Medical Image Segmentation](https://mlsanity.com/paper/2108.00402/) (2021)
- [A Large Scale Benchmark for Test Time Adaptation Methods in Medical Image Segmentation](https://mlsanity.com/paper/2512.02497/) (2025)
- [MLN-net: A multi-source medical image segmentation method for clustered microcalcifications using multiple layer normalization](https://mlsanity.com/paper/2309.02742/) (2023)
- [Framework Construction of an Adversarial Federated Transfer Learning Classifier](https://mlsanity.com/paper/2211.04734/) (2022)
- [S3-TTA: Scale-Style Selection for Test-Time Augmentation in Biomedical Image Segmentation](https://mlsanity.com/paper/2310.16783/) (2023)
- [Cross-Modal Ultrasound-MRI Learning for Fetal Brain Ventricular Volumetry and Abnormality Screening](https://mlsanity.com/paper/2608.14763/) (2026)

## Abstract (arXiv)

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.