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MWG-UNet: Hybrid Deep Learning Framework for Lung Fields and Heart Segmentation in Chest X-ray Images

Authors :
Yu Lyu
Xiaolin Tian
Source :
Bioengineering, Vol 10, Iss 9, p 1091 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

Deep learning technology has achieved breakthrough research results in the fields of medical computer vision and image processing. Generative adversarial networks (GANs) have demonstrated a capacity for image generation and expression ability. This paper proposes a new method called MWG-UNet (multiple tasking Wasserstein generative adversarial network U-shape network) as a lung field and heart segmentation model, which takes advantages of the attention mechanism to enhance the segmentation accuracy of the generator so as to improve the performance. In particular, the Dice similarity, precision, and F1 score of the proposed method outperform other models, reaching 95.28%, 96.41%, and 95.90%, respectively, and the specificity surpasses the sub-optimal models by 0.28%, 0.90%, 0.24%, and 0.90%. However, the value of the IoU is inferior to the optimal model by 0.69%. The results show the proposed method has considerable ability in lung field segmentation. Our multi-organ segmentation results for the heart achieve Dice similarity and IoU values of 71.16% and 74.56%. The segmentation results on lung fields achieve Dice similarity and IoU values of 85.18% and 81.36%.

Details

Language :
English
ISSN :
23065354
Volume :
10
Issue :
9
Database :
Directory of Open Access Journals
Journal :
Bioengineering
Publication Type :
Academic Journal
Accession number :
edsdoj.0029c29e078d43af99bf452f8c89688b
Document Type :
article
Full Text :
https://doi.org/10.3390/bioengineering10091091