Intelligent CAD Based on Anatomical Classification

基于解剖分类的智能CAD

• 批准号: 15070209
• 负责人: NIKI Noboru
• 金额: $ 51.2万
• 依托单位: The University of Tokushima
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research on Priority Areas
• 财政年份: 2003
• 资助国家: 日本
• 起止时间: 2003 至 2006
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-15070209/
• 关键词: 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

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 … More 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

近来，在日本，医生诊断大量多探测器行CT图像是很大的负担。我们的项目旨在开发一个复杂的系统，为医生诊断大量的CT图像有效地通过CAD。本研究开发的主要项目如下：-建立多器官、多疾病的海量图像数据库。-基于三维CT图像的多疾病检测的发展。开发利用三维CT图像辅助诊断肺部疾病、心血管疾病、骨骼疾病的综合系统。本研究的主要目标疾病为肺癌、肺气肿、冠状动脉钙化、骨质疏松症。研究结果如下：1.多器官、多疾病的海量影像数据库的建设：在医学信息的二次利用中，必须得到各医疗机构伦理委员会的批准。委员会要求保护 ...更多信息 DICOM标题信息的个人信息的删除。我们开发了一种匿名化方法，可以灵活地遵守每个医疗站点指定的策略，然后，开发了一个系统，在每个医疗站点的大规模DICOM图像平滑匿名。该系统目前在五个医疗点运行。基于三维CT图像的多疾病检测的研究进展(2-1)多器官分割和定量分析：我们开发了可以分割解剖结构的方法，如骨骼、胸壁、纵隔、横膈、血管、支气管等。引入与胸部解剖相关的空间位置，开发了支气管命名和肺叶分割技术。(2-2)肺癌候选者的检测：我们使用低剂量多探测器行CT图像，基于从大病例到小病例的结节候选者的肺解剖信息开发了一种检测算法。(2-3)肺气肿候选者的检测：我们开发了一种方法来提取低剂量CT三维图像上肺部区域的低衰减区（LAA）。检测方法允许我们分析检测到的LAA的体积和分布模式。此外，可以跟踪LAA容积的时间间隔变化。(2-4)冠状动脉钙化候选检测：提出了一种自动识别冠状动脉高衰减区域的方法。为了提高检测的准确性，我们使用分割的主动脉和肺动脉区域来减少检测区域。(2-5)骨质疏松症候选者的检测：我们开发了一种胸椎的提取算法，以测量松质组织内的CT值和骨密度。通过比较在生成的基础上提取的松质组织内部的平均CT值，我们开发了一种区分异常和正常骨的算法.集成系统：我们一直在集成的检测算法到一个原型系统与图形用户界面。我们验证了原型系统的有效性，使用大型数据集与医学专家的合作。少

项目成果

Quantitative Classification of Small Pulmonary Adenocarcinomas based on CT Number Histogram Patterns

基于CT值直方图模式的小肺腺癌的定量分类

• 发表时间: 2005
• 期刊: I-ELCAP
• 作者: J. Wu;T. Takeda;Thet-Thet-Lwin;N. Sunaguchi;T. Fukami;T. Yuasa;M. Minami;T. Akatsuka;K.Minami
• 通讯作者: K.Minami

肺癌のヘリカルCT検診におけるCAD

CAD在螺旋CT肺癌筛查中的应用

• 发表时间: 2004
• 期刊: 臨床放射線 Vol.49・No.3
• 作者: Doi;A.;仁木 登
• 通讯作者: 仁木 登

A Prospective study of CAD system for lung cancer based on helical CT image

基于螺旋CT图像的肺癌CAD系统的前瞻性研究

• 发表时间: 2004
• 期刊: Proc.SPIE Medical Imaging Vol.5372
• 作者: 小田敍弘;木戸尚治;庄野逸;大西利和;S. Urayama;M.Hasegawa
• 通讯作者: M.Hasegawa

Pulmonary nodule classification based on CT density distribution using 3D thoracic CT images

• DOI: 10.1117/12.535032
• 发表时间: 2004-04
• 作者: Y. Kawata;N. Niki;H. Ohamatsu;M. Kusumoto;R. Kakinuma;K. Mori;Kozo Yamada;H. Nishiyama;K. Eguchi;M. Kaneko;N. Moriyama

Construction Method of Three-dimensional Deformable Template Models for Tree-shaped Organs

树状器官三维变形模板模型的构建方法

• 发表时间: 2006
• 期刊: IEICE Transactions on Information and Systems E89-D・1
• 作者: Hotaka Takizawa;Shinji Yamamoto
• 通讯作者: Shinji Yamamoto