Liu, Z, Yang, X, Gao, R et al. (9 more authors) (2020) Remove Appearance Shift for Ultrasound Image Segmentation via Fast and Universal Style Transfer. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 03-07 Apr 2020, Iowa City, Iowa, USA. IEEE , pp. 1824-1828. ISBN 978-1-5386-9330-8
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.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Style transfer, Image segmentation, Ultrasound image, Appearance shift, Generalization ability |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 12 Nov 2021 14:28 |
Last Modified: | 26 Nov 2021 09:46 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/isbi45749.2020.9098457 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:180014 |