Super-Resolution and Segmentation of 3D Brain MR images using Multi-Channel data
| Project/Area Number |
18K18078
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 61010:Perceptual information processing-related
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| Research Institution | Ritsumeikan University |
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Principal Investigator |
Iwamoto Yutaro 立命館大学, 情報理工学部, 助教 (30779054)
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2020: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2019: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2018: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
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| Keywords | 医用画像処理 / 画質改善 / 超解像技術 / 深層学習 / セグメンテーション / マルチチャンネルデータ / 教師なし学習 / クラス分類 / 脳画像解析 / 高精細化 / 領域分割 |
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| Outline of Final Research Achievements |
Multi-channel data (T1-weighted image and T2-weighted image, etc.) is useful for high-precision medical image analysis such as segmentation of 3D brain MR images. However, due to the limitation of acquisition time of clinical requirements, these data are difficult to acquire with the same resolution. In this study, we proposed a super-resolution of MR images using deep learning. The proposed method enhances the resolution of low-resolution T2-weighted images by referring to high-resolution T1-weighted images. The proposed method can achieve better performance compared with several state-of-the-art methods. Furthermore, we also incorporate an unsupervised approach without high-resolution T2-weighted images as training data.
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| Academic Significance and Societal Importance of the Research Achievements |
近年複数のモダリティ画像(CT、MRI、PETなど)を用いた医用画像解析が盛んに行われている。これらのデータはモダリティ毎に解像度が異なることが多く、医用画像解析の前処理として解像度を揃える必要がある。従来は補間技術により解像度を合わせていたがエッジ領域のぼけやジャギなどが発生するため、高精度な領域分割(セグメンテーション)などでは問題となる。本課題はこの問題を解決することができる。また、医用画像のみならず解像度の異なる複数のセンサで取得されるデータ(カラー画像-深度画像、パンクロマティック画像-ハイパースペクトル画像)に対しても応用することができるため、研究の意義は大きい。
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Report
(4 results)
Research Products
(21 results)
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[Presentation] Multimodal Priors Guided Segmentation of Liver Lesions in MRI Using Mutual Information Based Graph Co-Attention Networks2020
Author(s)
Shaocong Mo, Ming Cai, Lanfen Lin, Ruofeng Tong, Qingqing Chen, Fang Wang, Hongjie Hu, Yutaro Iwamoto, Xian-Hua Han, Yen-Wei Chen
Organizer
Medical Image Computing and Computer Assisted Intervention, MICCAI 2020
Related Report
Int'l Joint Research
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[Presentation] UNET 3+: A Full-Scale Connected UNET for Medical Image Segmentation2020
Author(s)
Huiming Huang, Lanfen Lin, Ruofeng Tong, Hongjie Hu, Qiaowei Zhang, Yutaro Iwamoto, Xian-Hua Han, Yen-Wei Chen, Jian Wu
Organizer
Proc. of the 45th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP2020
Related Report
Int'l Joint Research
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