Big improvement of the generalization ability and robustness in construction of hybrid hierarchical statistical shape model of anatomical structures
| Project/Area Number |
26330191
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Perceptual information processing
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| Research Institution | Nagoya Institute of Technology |
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Principal Investigator |
Hontani Hidekata 名古屋工業大学, 工学(系)研究科(研究院), 教授 (60282688)
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| Project Period (FY) |
2014-04-01 – 2017-03-31
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| Project Status |
Completed (Fiscal Year 2016)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2016: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2015: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2014: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | 統計モデリング / モデル選択 / 医用画像処理 / 統計モデル / 汎化性能 / q-正規分布 / AIC規準 / Point Distribution Model / 画像セグメンテーション |
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| Outline of Final Research Achievements |
In this project, we developed a new method for constructing statistical shape model of anatomical organs robustly by employing not a normal distribution but a q-exponential distribution for the representation of the variety of the shape in order for estimating appropriate values of parameters even when the number of training data is small. A statistical shape model is constructed from a set of training data and it is not easy to collect the training data as many as the degree of the freedom of the shape variety. When the training data set is small and one employs a normal distribution for the representation, the resultant model overfits to the small training data set and the representation ability is degraded. Each layer of many conventional hierarchical models for the organs is mainly represented by a normal distribution. Using the results of this research, one can automatically select the best representation that maximizes the generalization ability.
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Report
(4 results)
Research Products
(18 results)
