Extraction of Whole-lung Field From Pseudo-chest X-ray Images Using U-net

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Title
Extraction of Whole-lung Field From Pseudo-chest X-ray Images Using U-net
Creator
細越 翔太
松尾 和明
Source Title
Shimane Journal of Medical Science
Volume 41
Issue 3
Start Page 63
End Page 71
Journal Identifire
ISSN 03865959
EISSN 24332410
Descriptions
This study aimed to develop a model using U-net to extract the whole lung field from pseudo-chest X-ray images, including areas overlapping with cardiac and diaphragm shadows. Training involved pseudo-X-rays and lung label images from CT scans of 140 cases from the LIDC-IDRI dataset. The extraction performance of the model was evaluated using the Dice similarity coefficient (DSC). We also examined the correlations among patient size, lung volume, and DSC. As a result, the whole-lung field extraction model developed in this study tended to over-extract intestinal gas in some cases, and the extraction performance varied depending on the patient size. However, the DSC between the whole-lung label image and the output image was >0.9 for all the test data, indicating that the whole-lung field can be extracted from the pseudo chest X-ray image.
Subjects
computed tomography
chest X-ray image
whole-lung field
segmentation
deep learning
Language
eng
Resource Type departmental bulletin paper
Publisher
Faculty of Medicine, Shimane University
島根大学医学部
Date of Issued 2024-09
Rights
Faculty of Medicine, Shimane University
権利関係(リンク) Creative Commons License
This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Publish Type Version of Record
Access Rights open access
Relation
[NCID] AA00841586
[DOI] 10.51010/sjms.41.3_63