Automatic co-segmentation of lung tumor based on random forest in PET-CT images

Xueqing Jiang; Dehui Xiang; Bin Zhang; Weifang Zhu; Fei Shi; Xinjian Chen

doi:10.1117/12.2216361

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21 March 2016 Automatic co-segmentation of lung tumor based on random forest in PET-CT images

Xueqing Jiang, Dehui Xiang, Bin Zhang, Weifang Zhu, Fei Shi, Xinjian Chen

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Proceedings Volume 9784, Medical Imaging 2016: Image Processing; 97842W (2016) https://doi.org/10.1117/12.2216361
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

In this paper, a fully automatic method is proposed to segment the lung tumor in clinical 3D PET-CT images. The proposed method effectively combines PET and CT information to make full use of the high contrast of PET images and superior spatial resolution of CT images. Our approach consists of three main parts: (1) initial segmentation, in which spines are removed in CT images and initial connected regions achieved by thresholding based segmentation in PET images; (2) coarse segmentation, in which monotonic downhill function is applied to rule out structures which have similar standardized uptake values (SUV) to the lung tumor but do not satisfy a monotonic property in PET images; (3) fine segmentation, random forests method is applied to accurately segment the lung tumor by extracting effective features from PET and CT images simultaneously. We validated our algorithm on a dataset which consists of 24 3D PET-CT images from different patients with non-small cell lung cancer (NSCLC). The average TPVF, FPVF and accuracy rate (ACC) were 83.65%, 0.05% and 99.93%, respectively. The correlation analysis shows our segmented lung tumor volumes has strong correlation ( average 0.985) with the ground truth 1 and ground truth 2 labeled by a clinical expert.

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Xueqing Jiang, Dehui Xiang, Bin Zhang, Weifang Zhu, Fei Shi, and Xinjian Chen "Automatic co-segmentation of lung tumor based on random forest in PET-CT images", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97842W (21 March 2016); https://doi.org/10.1117/12.2216361

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