29 March 2013 3D segmentation and image annotation for quantitative diagnosis in lung CT images with pulmonary lesions
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Abstract
Pulmonary nodules and ground glass opacities are highly significant findings in high-resolution computed tomography (HRCT) of patients with pulmonary lesion. The appearances of pulmonary nodules and ground glass opacities show a relationship with different lung diseases. According to corresponding characteristic of lesion, pertinent segment methods and quantitative analysis are helpful for control and treat diseases at an earlier and potentially more curable stage. Currently, most of the studies have focused on two-dimensional quantitative analysis of these kinds of deceases. Compared to two-dimensional images, three-dimensional quantitative analysis can take full advantage of isotropic image data acquired by using thin slicing HRCT in space and has better quantitative precision for clinical diagnosis. This presentation designs a computer-aided diagnosis component to segment 3D disease areas of nodules and ground glass opacities in lung CT images, and use AIML (Annotation and image makeup language) to annotate the segmented 3D pulmonary lesions with information of quantitative measurement which may provide more features and information to the radiologists in clinical diagnosis.
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Suo Li, Suo Li, Yanjie Zhu, Yanjie Zhu, Jianyong Sun, Jianyong Sun, Jianguo Zhang, Jianguo Zhang, } "3D segmentation and image annotation for quantitative diagnosis in lung CT images with pulmonary lesions", Proc. SPIE 8674, Medical Imaging 2013: Advanced PACS-based Imaging Informatics and Therapeutic Applications, 86740Y (29 March 2013); doi: 10.1117/12.2007469; https://doi.org/10.1117/12.2007469
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