@inproceedings{ad1c5e92329841baac18a8974996968d,
title = "DA-GAN: Learning structured noise removal in ultrasound volume projection imaging for enhanced spine segmentation",
abstract = "Ultrasound volume projection imaging (VPI) has shown to be appealing from a clinical perspective, because of its harmlessness, flexibility, and efficiency in scoliosis assessment. However, the limitations in hardware devices degrade the resultant image content with strong structured noise. Owing to the unavailability of reference data and the unpredictable degradation model, VPI image recovery is a challenging problem. In this paper, we propose a novel framework to learn the structured noise removal from unpaired samples. We introduce the attention mechanism into the generative adversarial network to enhance the learning by focusing on the salient corrupted patterns. We also present a dual adversarial learning strategy and integrate the denoiser with a segmentation model to produce the task-oriented noiseless estimation. Experimental results show that the proposed method can improve both the visual quality and the segmentation accuracy on spine images. ",
keywords = "Spine segmentation, Ultrasound image restoration, Unpaired learning",
author = "Zixun Huang and Rui Zhao and Leung, {Frank H.F.} and Lam, {Kin Man} and Ling, {Sai Ho} and Juan Lyu and Sunetra Banerjee and Lee, {Timothy Tin Yan} and De Yang and Zheng, {Yong Ping}",
note = "Funding Information: This work was partially supported by Hong Kong Research Grant Council Research Impact Fund (R5017-18), and a grant from The Hong Kong Polytechnic University. The author Zheng YP owned a number of patents related to the Scol-ioscan system, which have been licensed to Telefield Medical Imaging Limited for commercialization. Publisher Copyright: {\textcopyright} 2021 IEEE.; 18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 ; Conference date: 13-04-2021 Through 16-04-2021",
year = "2021",
month = apr,
day = "13",
doi = "10.1109/ISBI48211.2021.9434136",
language = "English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "770--774",
booktitle = "2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021",
address = "United States",
}