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ファイル
言語
英語
属性
Original Article
著者
細越 翔太
松尾 和明
内容記述(抄録等)
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.
主題
computed tomography
chest X-ray image
whole-lung field
segmentation
deep learning
掲載誌名
Shimane Journal of Medical Science
41
3
開始ページ
63
終了ページ
71
ISSN
03865959
ISSN(Online)
24332410
発行日
2024-09
NCID
AA00841586
DOI
出版者
Faculty of Medicine, Shimane University
出版者別表記
島根大学医学部
資料タイプ
紀要論文
ファイル形式
PDF
権利関係
Faculty of Medicine, Shimane University
権利関係(リンク)
著者版/出版社版
出版社版
部局
医学部
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