File |
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Title |
Extraction of Whole-lung Field From Pseudo-chest X-ray Images Using U-net
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Creator |
細越 翔太
松尾 和明
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Source Title |
Shimane Journal of Medical Science
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Volume | 41 |
Issue | 3 |
Start Page | 63 |
End Page | 71 |
Journal Identifire |
ISSN 03865959
EISSN 24332410
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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.
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Subjects |
computed tomography
chest X-ray image
whole-lung field
segmentation
deep learning
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Language |
eng
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Resource Type | departmental bulletin paper |
Publisher |
Faculty of Medicine, Shimane University
島根大学医学部
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Date of Issued | 2024-09 |
Rights |
Faculty of Medicine, Shimane University
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権利関係(リンク) | ![]() 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
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