Intelligent CAD Based on Anatomical Classification

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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