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

最近，在日本，诊断大量的多排CT图像给医生带来了很大的负担。我们的项目旨在为医生开发一个复杂的系统，通过CAD有效地诊断大量的CT图像。本研究开发的主要项目如下：——构建多器官、多疾病的海量图像数据库。-基于三维CT图像的多种疾病检测技术的发展。-开发利用3-D CT影像诊断肺部疾病、心脏血管疾病、骨骼疾病的综合系统。本研究检测的主要靶病为肺癌、肺气肿、冠状动脉钙化、骨质疏松症。研究结果如下：庞大的多器官、多疾病影像数据库的建设：医学信息在二次利用中用于科研和教学，必须得到各医疗现场伦理委员会的批准。委员会要求对DICOM头信息的个人信息进行更多的处理。在此基础上，我们开发了一种灵活的匿名化方法，使其符合各个医疗站点指定的策略，并在此基础上开发了一个能够对各个医疗站点的大规模DICOM图像进行平滑匿名化的系统。该系统目前在5个医疗点运行。基于三维CT图像的多种疾病检测研究进展。（2-1）多器官分割与定量分析：我们开发了可以分割骨骼、胸壁、纵隔、隔膜、血管、支气管等解剖结构的方法。介绍了胸椎解剖中的空间定位、支气管命名和肺叶分割技术。（2-2）肺癌候选病灶的检测：我们利用低剂量多排CT图像，基于从大病例到小病例GGO候选病灶的肺解剖信息，开发了一种检测算法。（2-3）肺气肿候选病灶的检测：我们开发了一种在三维低剂量CT图像上提取肺区域低衰减区（LAA）的方法。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，还可以跟踪LAA量的时间间隔变化。（2-4）冠状动脉候选钙化检测：我们开发了一种提取冠状动脉高衰减区域并自动识别的方法。为了提高检测精度，我们利用分割的主动脉和肺动脉区域来缩小检测面积。（2-5）候选骨质疏松症的检测：我们开发了胸椎的提取算法，测量松质组织内部的CT数和骨密度。在生成的基础上，通过对提取的松质组织内部CT平均值的比较，我们开发了一种区分异常与正常骨骼的算法。集成系统：我们已经将检测算法集成到具有图形用户界面的原型系统中。在医学专家的合作下，我们使用大型数据集验证了原型系统的有效性。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