| Project/Area Number |
15070209
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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 | The University of Tokushima |
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Principal Investigator |
NIKI Noboru The University of Tokushima, Graduate School, Institute of technology and science, Professor (80116847)
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| Co-Investigator(Kenkyū-buntansha) |
KUBO Mitsuru University of Tokushima, Graduate School, Institute of technology and science, Assistant Professor (30325245)
NISHITANI Hiromu University of Tokushima, Graduate School, Institute of Health Biosciences, Professor (50117206)
EGUCHI Kenji Tokai University, School of medicine, Professor (30349336)
NAKANO Yasutaka Shiga University, Medical Science, Lecturer (00362377)
OHMATSU Hironobu National cancer center, 国立がんセンター, Researcher (40415518)
河田 佳樹 徳島大学, 大学院・ソシオテクノサイエンス研究部, 助教授 (70274264)
上野 淳二 徳島大学, 医学部, 教授 (60116788)
山本 眞司 中京大学, 情報理工学部, 教授 (80230556)
片田 和廣 藤田保健衛生大学, 医学部, 教授 (00101684)
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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 |
¥80,000,000 (Direct Cost: ¥80,000,000)
Fiscal Year 2006: ¥20,000,000 (Direct Cost: ¥20,000,000)
Fiscal Year 2005: ¥20,000,000 (Direct Cost: ¥20,000,000)
Fiscal Year 2004: ¥20,000,000 (Direct Cost: ¥20,000,000)
Fiscal Year 2003: ¥20,000,000 (Direct Cost: ¥20,000,000)
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| Keywords | Intelligent CAD / Anatomical classification / Image database / Multi-organ, multi-disease / Lung・heart・bone diseases / 解剖学的分類 / 大規模症例画像データベース / 胸部3次元CT画像 / 知的CADシステム / 検出・診断アルゴリズム / デジタル診断環境 / マルチスライスCT / 肺がん / 検出 / 鑑別 / 肺気腫 / 冠動脈石灰化 / 骨粗鬆症 / 計算機支援診断 / 3次元CT画像 / コンピュータ支援診断 / 検出と分類 / 画像処理 |
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| Research Abstract |
Recently, it is a large amount of burden for physicians to diagnose massive multi-detector row CT images in Japan. Our project aims to develop a sophisticated system for physicians to diagnose a large amount of the CT images effectively through CAD. Principal items of this research and development are as follows:
-Construction of massive image database of multi-organ, multi-disease.
-Development of multi-diseases detection based on 3-D CT images.
-Development of integrated system that aids physicians to diagnose lung diseases, heart vessel diseases, and bone diseases using 3-D CT images.
In this study, main target diseases for detection are lung cancer, pulmonary emphysema, calcification of coronary artery, and osteoporosis. Research results are as follows.
1. Construction of massive image database of multi-organ, multi-disease: In secondary use of medical information for research and education, approval by ethics committee in each medical site is essential. The committee requires the prote
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ction of personal information of DICOM header information. We developed anonymization method which can flexibly comply with the policy that specified by each medical site and then, developed a system which smoothly anonymizes large-scale DICOM images in each medical site. The system is now operating in five medical site.
2. Development of multi-diseases detection based on 3-D CT images.
(2-1) multi-organ segmentation and quantitative analyses: We developed methods that can segment anatomical structures such as bone, chest wall, mediastinum, diaphragm, vessel, bronchus, etc. Introducing spatial location concerning the thoracic anatomy, bronchus nomenclation and lung lobe segmentation techniques were developed.
(2-2) Detection of candidate of lung cancer: We developed a detection algorithm based on the lung anatomical information of nodule candidates from large cases to small cases with GGO using low-dose multi-detector row CT images.
(2-3) Detection of candidate of pulmonary emphysema: We developed a method to extract low attenuation areas (LAA) in lung region on 3-D low-dose CT images. The detection method allows us to analyze volume and distribution patterns of the detected LAA. Additionally, it is possible to trace the time interval change of LAA volume.
(2-4) Detection of calcification candidate of coronary artery: We developed a method to extract high attenuation areas on coronary artery that is automatically identified. In order to improve the detection accuracy, we reduced detection area by using the segmented aorta and pulmonary artery region.
(2-5) Detection of candidate of osteoporosis: We developed an extraction algorithm of thoracic vertebrae to measure CT number inside cancellous tissue and bone density. From the comparison of the average CT value inside the extracted cancellous tissue on the basis of the generation, we developed an algorithm for differentiating abnormalities from normal bones.
3. Integrated system: We have been integrating the detection algorithms into a prototype system with graphical user interface. We validated the effectiveness of the prototype system using a large dataset with the cooperation of medical experts. Less
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