Automated localization and segmentation of lung tumor from PET-CT thorax volumes based on image feature analysis.
Positron emission tomography - computed tomography (PET-CT) plays an essential role in early tumor detection, diagnosis, staging and treatment. Automated and more accurate lung tumor detection and delineation from PET-CT is challenging. In this paper, on the basis of quantitative analysis of contrast feature of PET volume in SUV (standardized uptake value), our method firstly automatically localized the lung tumor. Then based on analysing the surrounding CT features of the initial tumor definition, our decision strategy determines the tumor segmentation from CT or from PET. The algorithm has been validated on 20 PET-CT studies involving non-small cell lung cancer (NSCLC). Experimental results demonstrated that our method was able to segment the tumor when adjacent to mediastinum or chest wall, and the algorithm outperformed the other five lung segmentation methods in terms of overlapping measure.
Biomedical and Multimedia Information Technology (BMIT) research group, School of Information Technologies, The University of Sydney, Australia. hcui7511@uni.sydney.edu.au
Hui
H
Cui
Xiuying
X
Wang
Dagan
D
Feng
D000465:Algorithms; D001331:Automation; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D064847:Multimodal Imaging / Q000379:methods*; D049268:Positron-Emission Tomography; D014057:Tomography, X-Ray Computed