MDU-Net: A Convolutional Network for Clavicle and Rib Segmentation from a Chest Radiograph
Joint Authors
Feng, Jun
Cui, Lei
Wang, Wenjing
Feng, Hongwei
Bu, Qirong
Xie, Yilin
Zhang, Aoqi
Zhu, Zhaohui
Chen, Zhongyuanlong
Source
Journal of Healthcare Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-17
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Automatic bone segmentation from a chest radiograph is an important and challenging task in medical image analysis.
However, a chest radiograph contains numerous artifacts and tissue shadows, such as trachea, blood vessels, and lung veins, which limit the accuracy of traditional segmentation methods, such as thresholding and contour-related techniques.
Deep learning has recently achieved excellent segmentation of some organs, such as the pancreas and the hippocampus.
However, the insufficiency of annotated datasets impedes clavicle and rib segmentation from chest X-rays.
We have constructed a dataset of chest X-rays with a raw chest radiograph and four annotated images showing the clavicles, anterior ribs, posterior ribs, and all bones (the complete set of ribs and clavicle).
On the basis of a sufficient dataset, a multitask dense connection U-Net (MDU-Net) is proposed to address the challenge of bone segmentation from a chest radiograph.
We first combine the U-Net multiscale feature fusion method, DenseNet dense connection, and multitasking mechanism to construct the proposed network referred to as MDU-Net.
We then present a mask encoding mechanism that can force the network to learn the background features.
Transfer learning is ultimately introduced to help the network extract sufficient features.
We evaluate the proposed network by fourfold cross validation on 88 chest radiography images.
The proposed method achieves the average DSC (Dice similarity coefficient) values of 93.78%, 80.95%, 89.06%, and 88.38% in clavicle segmentation, anterior rib segmentation, posterior rib segmentation, and segmentation of all bones, respectively.
American Psychological Association (APA)
Wang, Wenjing& Feng, Hongwei& Bu, Qirong& Cui, Lei& Xie, Yilin& Zhang, Aoqi…[et al.]. 2020. MDU-Net: A Convolutional Network for Clavicle and Rib Segmentation from a Chest Radiograph. Journal of Healthcare Engineering،Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1186234
Modern Language Association (MLA)
Wang, Wenjing…[et al.]. MDU-Net: A Convolutional Network for Clavicle and Rib Segmentation from a Chest Radiograph. Journal of Healthcare Engineering No. 2020 (2020), pp.1-9.
https://search.emarefa.net/detail/BIM-1186234
American Medical Association (AMA)
Wang, Wenjing& Feng, Hongwei& Bu, Qirong& Cui, Lei& Xie, Yilin& Zhang, Aoqi…[et al.]. MDU-Net: A Convolutional Network for Clavicle and Rib Segmentation from a Chest Radiograph. Journal of Healthcare Engineering. 2020. Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1186234
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1186234