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Dual Encoder Fusion U-Net (DEFU-Net) for Cross-manufacturer Chest X-Ray Segmentation

Zhang, L; Liu, A; Xiao, J; Taylor, P; (2021) Dual Encoder Fusion U-Net (DEFU-Net) for Cross-manufacturer Chest X-Ray Segmentation. In: 2020 25th International Conference on Pattern Recognition (ICPR). IEEE: Milan, Italy. (In press). Green open access

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Abstract

—A number of methods based on deep learning have been applied to medical image segmentation and have achieved state-of-the-art performance. Due to the importance of chest x-ray data in studying COVID-19, there is a demand for state-of-the-art models capable of precisely segmenting soft tissue on the chest x-rays. The dataset for exploring best segmentation model is from Montgomery and Shenzhen hospital which had opened in 2014. The most famous technique is UNet which has been used to many medical datasets including the Chest X-rays. However, most variant U-Nets mainly focus on extraction of contextual information and skip connections. There is still a large space for improving extraction of spatial features. In this paper, we propose a dual encoder fusion U-Net framework for Chest X-rays based on Inception Convolutional Neural Network with dilation, Densely Connected Recurrent Convolutional Neural Network, which is named DEFU-Net. The densely connected recurrent path extends the network deeper for facilitating contextual feature extraction. In order to increase the width of network and enrich representation of features, the inception blocks with dilation are adopted. The inception blocks can capture globally and locally spatial information from various receptive fields. At the same time, the two paths are fused by summing features, thus preserving the contextual and spatial information for decoding part. This multi-learning-scale model is benefiting in Chest X-ray dataset from two different manufacturers (Montgomery and Shenzhen hospital). The DEFUNet achieves the better performance than basic U-Net, residual U-Net, BCDU-Net, R2U-Net and attention R2U-Net. This model has proved the feasibility for mixed dataset and approaches stateof-the-art. The source code for this proposed framework is public https://github.com/uceclz0/DEFU-Net

Type: Proceedings paper
Title: Dual Encoder Fusion U-Net (DEFU-Net) for Cross-manufacturer Chest X-Ray Segmentation
Event: International Conference on Pattern Recognition
Location: Milano
Dates: 10 January 2021 - 15 January 2021
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ICPR48806.2021.9412718
Publisher version: https://doi.org/ 10.1109/ICPR48806.2021.9412718
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
Keywords: Medical Imaging, Lung Segmentation, Convolutional Neural Network, U-Net, DEFU-Net
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > CHIME
URI: https://discovery.ucl.ac.uk/id/eprint/10122712
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