A 3D Segmentation Method for Pulmonary Nodule Image Sequences based on Supervoxels and Multimodal Data

doi:10.23940/ijpe.17.05.p12.682696

Abstract

Abstract: Three-dimensional reconstruction can reflect the dynamic relationship between lung lesions and surrounding tissues. It is easy to obtain an intuitive understanding of the shape, size, appearance and surroundings of pulmonary nodules, such as pleura or blood vessels. Three-dimensional reconstruction greatly improves the quality of surgery and reduces risk. This technique can help doctors to understand disease better and can guide operations in complex anatomical areas; therefore, it is worth recommending its clinical use. Therefore, our paper proposes a 3D segmentation method for use with pulmonary nodule image sequences based on supervoxels and multimodal data. First, we segment the lung parenchyma into superpixels. Then, we register PET/CT images using mutual information to roughly locate pulmonary nodule areas, matching the accurate pulmonary nodule areas using a multi-scale circular template matching algorithm. Finally, an improved three-dimensional supervoxel region-growing algorithm is proposed to reconstruct three-dimensional pulmonary nodules. The experimental results show that compared with the 3D region-growing algorithm, our algorithm can reconstruct complex pulmonary nodules more accurately and reduce time complexity.

Submitted on April 20, 2017; Revised on June 13, 2017; Accepted on August 20, 2017
References: 35

Qiang Cui, Zinlin Qiang, Juanjuan Zhao, Yan Qiang, and Xiaolei Liao. A 3D Segmentation Method for Pulmonary Nodule Image Sequences based on Supervoxels and Multimodal Data [J]. Int J Performability Eng, 2017, 13(5): 682-696.

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