A deep learning based dual encoder–decoder framework for anatomical structure segmentation in chest X-ray images

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 sh...

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Vydáno v:Scientific reports Ročník 13; číslo 1; s. 791 - 14
Hlavní autoři: Ullah, Ihsan, Ali, Farman, Shah, Babar, El-Sappagh, Shaker, Abuhmed, Tamer, Park, Sang Hyun
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 16.01.2023
Nature Publishing Group
Nature Portfolio
Témata:
631/114/1305
631/114/1564
Chest
Deep Learning
Fluoroscopy
Humanities and Social Sciences
Humans
Image processing
Image Processing, Computer-Assisted - methods
multidisciplinary
Neural networks
Neural Networks, Computer
Pacemakers
Science
Science (multidisciplinary)
Segmentation
Thorax - diagnostic imaging
X-Rays
ISSN:2045-2322, 2045-2322
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Shrnutí: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.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-27815-w