File | |
language |
eng
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Attribute |
Original Article
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Author |
細越 翔太
松尾 和明
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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.
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Subject | computed tomography
chest X-ray image
whole-lung field
segmentation
deep learning
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Journal Title |
Shimane Journal of Medical Science
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Volume | 41
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Issue | 3
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Start Page | 63
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End Page | 71
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ISSN | 03865959
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ISSN(Online) | 24332410
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Published Date | 2024-09
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NCID | AA00841586
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DOI | |
Publisher | Faculty of Medicine, Shimane University
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Publisher Aalternative | 島根大学医学部
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NII Type |
Departmental Bulletin Paper
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Format |
PDF
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Rights | Faculty of Medicine, Shimane University
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rights(link) | This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. |
Text Version |
出版社版
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OAI-PMH Set |
Faculty of Medicine
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他の一覧 |