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Real-time markerless tumour tracking with patient-specific deep learning using a personalized data generation strategy: Proof of concept by phantom study
https://repo.qst.go.jp/records/79593
https://repo.qst.go.jp/records/79593330b9ab0-c1aa-4182-b660-1b0f168e8d39
| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2020-02-10 | |||||
| タイトル | ||||||
| タイトル | Real-time markerless tumour tracking with patient-specific deep learning using a personalized data generation strategy: Proof of concept by phantom study | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
| 資源タイプ | journal article | |||||
| アクセス権 | ||||||
| アクセス権 | metadata only access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_14cb | |||||
| 著者 |
Takahashi, Wataru
× Takahashi, Wataru× Oshikawa, Shota× Mori, Shinichiro× Mori, Shinichiro |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Purpose Large amounts of subject data are required for training data sets in deep learning (DL) for medical imaging. Collection of these data is crucial, but some collection strategies are hampered by the heterogeneity of subject data. For real-time markerless tumour tracking, we propose a different approach which uses patient-specific DL using a personalized data generation strategy. Methods We developed lung tumour tracking for radiotherapy using a convolutional neural network trained for each patient’s lesion, avoiding the need for a large data set by using multiple digitally reconstructed radiographs (DRRs) generated from each patient’s treatment planning 4D-CT. We trained tumour-bone differentiation using huge training DRRs generated with different projection geometries to simulate tumour motion. We solved the problem of using DRRs for training and X-ray images for tracking by using the training DRRs with random contrast transformation and random noise addition. These solutions also double as data augmentation. Results We defined adequate target coverage (TC) as the % frames satisfying < 1 mm tracking accuracy of the isocentre. In a simulation study, we achieved 100% TC in 3 cm spherical and 1.5 × 2.25 × 3 cm ovoid masses. In a phantom study, we achieved 100% and 94.7% TC in 3- and 2-cm spherical masses, respectively. This required 32.5 ms/frame (30.8 fps) real-time processing. Conclusions We proposed and validated a real-time markerless tumour tracking framework based on patient-specific DL with a personalized data generation strategy. Advances in Knowledge Using DL with personalized data generation is an efficient method for real-time tumour tracking. |
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| 書誌情報 |
British Journal of Radiology 巻 93, 号 1109, 発行日 2020-03 |
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| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 0007-1285 | |||||
| DOI | ||||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | 10.1259/bjr.20190420 | |||||
| 関連サイト | ||||||
| 識別子タイプ | URI | |||||
| 関連識別子 | https://www.birpublications.org/doi/10.1259/bjr.20190420 | |||||
