| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2026-01-21 |
| タイトル |
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タイトル |
F-FDG PET-based liver segmentation using deep-learning. |
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言語 |
en |
| 言語 |
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言語 |
eng |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
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資源タイプ |
journal article |
| 著者 |
Yuta Kaneko
Kenta Miwa
Tensho Yamao
Noriaki Miyaji
Ryuichi Nishii
Kana Yamazaki
Noriko Nishikawa
Masanori Yusa
Tatsuya Higashi
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| 抄録 |
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内容記述タイプ |
Abstract |
|
内容記述 |
Organ segmentation using F-FDG PET images alone has not been extensively explored. Segmentation based methods based on deep learning (DL) have traditionally relied on CT or MRI images, which are vulnerable to alignment issues and artifacts. This study aimed to develop a DL approach for segmenting the entire liver based solely on F-FDG PET images. We analyzed data from 120 patients who were assessed using F-FDG PET. A three-dimensional (3D) U-Net model from nnUNet and preprocessed PET images served as DL and input images, respectively, for the model. The model was trained with 5-fold cross-validation on data from 100 patients, and segmentation accuracy was evaluated on an independent test set of 20 patients. Accuracy was assessed using Intersection over Union (IoU), Dice coefficient, and liver volume. Image quality was evaluated using mean (SUVmean) and maximum (SUVmax) standardized uptake value and signal-to-noise ratio (SNR). The model achieved an average IoU of 0.89 and an average Dice coefficient of 0.94 based on test data from 20 patients, indicating high segmentation accuracy. No significant discrepancies in image quality metrics were identified compared with ground truth. Liver regions were accurately extracted from F-FDG PET images which allowed rapid and stable evaluation of liver uptake in individual patients without the need for CT or MRI assessments. |
| 書誌情報 |
Physical and engineering sciences in medicine
巻 48,
号 3,
p. 1415-1424,
発行日 2025-09
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| ISSN |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
2662-4737 |
| PubMed番号 |
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識別子タイプ |
PMID |
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関連識別子 |
40665198 |
| DOI |
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識別子タイプ |
DOI |
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関連識別子 |
10.1007/s13246-025-01595-1 |
