Paper
29 March 2016 Preliminary study of visualizing membrane structures of spiculated pulmonary nodules in three-dimensional thoracic CT images
Y. Kawata, N. Niki, H. Ohmatsu, K. Aokage, M. Kusumoto, T. Tsuchida, K. Eguchi, M. Kaneko
Author Affiliations +
Abstract
Research results from the National Lung Screening Trial revealed that screening for lung cancer with low-dose CT (LDCT) reduces lung cancer mortality in heavy smokers by 20% compared to radiography. While this study does show the efficacy of CT-based screening, radiologists often face the problem of estimating the malignant likelihoods of pulmonary nodules detected on LDCT screening for maximizing patient survival and for preserving lung function. Spiculation is considered as one of the indicators of nodule malignancy and an important feature to assess requirements on a patient-tailored follow-up procedure. However, the spiculation is also observed in some benign nodules, particularly in tuberculoma. The elucidation of the spliculation morphology in 3D thoracic CT images is an important preliminary step towards developing the malignant discrimination strategies from benign nodules. In this study, we present a visualization method to reveal a spatial configuration of spiculation of pulmonary nodules in three-dimensional thoracic CT images. Applying the method to an example of malignant nodule with the spiculated margins, the visualizing preliminary result of the spatial configuration reveals the presence of membrane structures of spiculation.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Y. Kawata, N. Niki, H. Ohmatsu, K. Aokage, M. Kusumoto, T. Tsuchida, K. Eguchi, and M. Kaneko "Preliminary study of visualizing membrane structures of spiculated pulmonary nodules in three-dimensional thoracic CT images", Proc. SPIE 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, 978819 (29 March 2016); https://doi.org/10.1117/12.2216691
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KEYWORDS
Computed tomography

3D image processing

Visualization

Image segmentation

Lung cancer

Lung

3D image enhancement

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