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language
eng
Attribute
Original Article
Author
細越 翔太
松尾 和明
Description
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.
Subject
computed tomography
chest X-ray image
whole-lung field
segmentation
deep learning
Journal Title
Shimane Journal of Medical Science
Volume
41
Issue
3
Start Page
63
End Page
71
ISSN
03865959
ISSN(Online)
24332410
Published Date
2024-09
NCID
AA00841586
DOI
Publisher
Faculty of Medicine, Shimane University
Publisher Aalternative
島根大学医学部
NII Type
Departmental Bulletin Paper
Format
PDF
Rights
Faculty of Medicine, Shimane University
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