3 March 2009 Quantitative analysis of the central-chest lymph nodes based on 3D MDCT image data
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Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis; 72600U (2009) https://doi.org/10.1117/12.811040
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
Lung cancer is the leading cause of cancer death in the United States. In lung-cancer staging, central-chest lymph nodes and associated nodal stations, as observed in three-dimensional (3D) multidetector CT (MDCT) scans, play a vital role. However, little work has been done in relation to lymph nodes, based on MDCT data, due to the complicated phenomena that give rise to them. Using our custom computer-based system for 3D MDCT-based pulmonary lymph-node analysis, we conduct a detailed study of lymph nodes as depicted in 3D MDCT scans. In this work, the Mountain lymph-node stations are automatically defined by the system. These defined stations, in conjunction with our system's image processing and visualization tools, facilitate lymph-node detection, classification, and segmentation. An expert pulmonologist, chest radiologist, and trained technician verified the accuracy of the automatically defined stations and indicated observable lymph nodes. Next, using semi-automatic tools in our system, we defined all indicated nodes. Finally, we performed a global quantitative analysis of the characteristics of the observed nodes and stations. This study drew upon a database of 32 human MDCT chest scans. 320 Mountain-based stations (10 per scan) and 852 pulmonary lymph nodes were defined overall from this database. Based on the numerical results, over 90% of the automatically defined stations were deemed accurate. This paper also presents a detailed summary of central-chest lymph-node characteristics for the first time.
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Kongkuo Lu, Rebecca Bascom, Rickhesvar P. M. Mahraj, William E. Higgins, "Quantitative analysis of the central-chest lymph nodes based on 3D MDCT image data", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72600U (3 March 2009); doi: 10.1117/12.811040; https://doi.org/10.1117/12.811040
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