| Project/Area Number |
15070202
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| Research Category |
Grant-in-Aid for Scientific Research on Priority Areas
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| Allocation Type | Single-year Grants |
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| Review Section |
Science and Engineering
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| Research Institution | Tokyo University of Agriculture and Technology |
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Principal Investigator |
KOBATAKE Hidefumi Tokyo University of Agriculture and Technology, Headquarters, President (80013720)
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| Co-Investigator(Kenkyū-buntansha) |
SHIMIZU Akinobu Tokyo University of Agriculture and Technology, Institute of Symbiotic Science and Technology, Associate Professor (80262880)
NAWANO Shigeru National Cancer Center Hospital East, 放射線部, Radiology Chief of division (40156005)
HAGIWARA Yoshihiro Iwate University, Faculty of Engineering, Associate Professor (80293009)
黄 琳琳 国立大学法人東京農工大学, 大学院・共生科学技術研究部, 助手 (60359685)
中静 真 東京農工大学, 大学院・生物システム応用科学研究科, 助教授 (10251787)
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| Project Period (FY) |
2003 – 2006
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| Project Status |
Completed (Fiscal Year 2007)
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| Budget Amount *help |
¥68,000,000 (Direct Cost: ¥68,000,000)
Fiscal Year 2006: ¥17,000,000 (Direct Cost: ¥17,000,000)
Fiscal Year 2005: ¥17,000,000 (Direct Cost: ¥17,000,000)
Fiscal Year 2004: ¥17,000,000 (Direct Cost: ¥17,000,000)
Fiscal Year 2003: ¥17,000,000 (Direct Cost: ¥17,000,000)
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| Keywords | simultaneous segmentation / digital atlas of human anatomy / posterior probability / eigen-shape / multiple level set / multi-organ multi-disease CAD / CT / abdominal organ / Ai / 死亡画像 / 病理診断 / 診断支援 / CAD / 画像診断 / 画像認識 / セグメンテーション / 三次元モデル / 特徴抽出 / 肝臓 / じん臓 / 脾臓 / 臓器モデル |
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| Research Abstract |
We developed a digital atlas of human anatomy and a simultaneous segmentation algorithm for multiple organs. The digital atlas of human anatomy is a computational and statistical database including information about human anatomy, pathology and function, such as physiological and motility function. This study focused on twelve organs in three dimensional upper abdominal CT images, namely oesophagus, heart, stomach, liver, gallbladder, pancreas, left and right kidneys, spleen, portal vein, aorta, and inferior vena cava. We built 1) probabilistic atlases of the organs showing the probability of existence, 2) statistical shape model including average and eigen-shapes, and 3) statistical database of features, such as gray values in the CT images. We also developed a simultaneous segmentation algorithm of 12 abdominal organs based on the digital atlas of human anatomy. The algorithm consists of spatial normalization, rough extraction and fine extraction of the organs. A hierarchal spatial normalization process was proposed in this project, which normalized large organs first, then performed normalization for small organs whose spatial variation is large. The rough extraction process was based on maximum a posterior method. The method estimated probabilistic distribution parameters of features using a modified EM algorithm which used the probabilistic atlases as a priori information. We proposed a posterior probability based on features of neighboring voxels. Finally the proposed method performed a multiple level set method which extracted the twelve organs simultaneously based on mutual interaction between neighboring organs. We applied the proposed algorithm to abdominal CT volumes of 17 cases and confirmed that the Jaccard index was 75% on average which is quite high comparing to the previous method.
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