| Project/Area Number |
15K19775
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Radiation science
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| Research Institution | The University of Tokyo |
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Principal Investigator |
Hanaoka Shouhei 東京大学, 医学部附属病院, 特任講師 (80631382)
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| Project Period (FY) |
2015-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
Fiscal Year 2017: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2016: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2015: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
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| Keywords | 医用画像工学 / 転移性骨腫瘍 / X線CT / 深層学習 / コンピュータ支援検出 / 医用画像処理 / セグメンテーション / 異常検知 / 骨転移 / コンピュータ支援診断 / 放射線診断学 |
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| Outline of Final Research Achievements |
We developed a method to highlight bone metastases from CT datasets by using deep learning. When previous and current CT datasets are inputted, the algorithm can detect both osteolytic and osteoblastic metastases and output them. The algorithm is not a simple temporal subtraction, but it detects abnormalities using the estimated change (and deviation of the change) of the CT value both of which are calculated by a deep learning. Thus, the proposed method produces less false positives. Thanks to this, metastases are clearly shown in the proposed maximum intensity projection image, which helps radiologists to easily detect metastases.
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