Document Type
Article
Source of Publication
Scientific Reports
Publication Date
1-16-2023
Abstract
Automated multi-organ segmentation plays an essential part in the computer-aided diagnostic (CAD) of chest X-ray fluoroscopy. However, developing a CAD system for the anatomical structure segmentation remains challenging due to several indistinct structures, variations in the anatomical structure shape among different individuals, the presence of medical tools, such as pacemakers and catheters, and various artifacts in the chest radiographic images. In this paper, we propose a robust deep learning segmentation framework for the anatomical structure in chest radiographs that utilizes a dual encoder–decoder convolutional neural network (CNN). The first network in the dual encoder–decoder structure effectively utilizes a pre-trained VGG19 as an encoder for the segmentation task. The pre-trained encoder output is fed into the squeeze-and-excitation (SE) to boost the network’s representation power, which enables it to perform dynamic channel-wise feature calibrations. The calibrated features are efficiently passed into the first decoder to generate the mask. We integrated the generated mask with the input image and passed it through a second encoder–decoder network with the recurrent residual blocks and an attention the gate module to capture the additional contextual features and improve the segmentation of the smaller regions. Three public chest X-ray datasets are used to evaluate the proposed method for multi-organs segmentation, such as the heart, lungs, and clavicles, and single-organ segmentation, which include only lungs. The results from the experiment show that our proposed technique outperformed the existing multi-class and single-class segmentation methods.
DOI Link
ISSN
Publisher
Springer Science and Business Media LLC
Volume
13
Issue
1
First Page
791
Last Page
791
Disciplines
Computer Sciences | Medicine and Health Sciences
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Ullah, Ihsan; Ali, Farman; Shah, Babar; El-Sappagh, Shaker; Abuhmed, Tamer; and Park, Sang Hyun, "A deep learning based dual encoder–decoder framework for anatomical structure segmentation in chest X-ray images" (2023). All Works. 5569.
https://zuscholars.zu.ac.ae/works/5569
Indexed in Scopus
no
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series