title: Automatic lung tumor segmentation on PET/CT images using fuzzy Markov random field model.
abstract: The combination of positron emission tomography (PET) and CT images provides complementary functional and anatomical information of human tissues and it has been used for better tumor volume definition of lung cancer. This paper proposed a robust method for automatic lung tumor segmentation on PET/CT images. The new method is based on fuzzy Markov random field (MRF) model. The combination of PET and CT image information is achieved by using a proper joint posterior probability distribution of observed features in the fuzzy MRF model which performs better than the commonly used Gaussian joint distribution. In this study, the PET and CT simulation images of 7 non-small cell lung cancer (NSCLC) patients were used to evaluate the proposed method. Tumor segmentations with the proposed method and manual method by an experienced radiation oncologist on the fused images were performed, respectively. Segmentation results obtained with the two methods were similar and Dice's similarity coefficient (DSC) was 0.85 ± 0.013. It has been shown that effective and automatic segmentations can be achieved with this method for lung tumors which locate near other organs with similar intensities in PET and CT images, such as when the tumors extend into chest wall or mediastinum.
authors.affiliation: Tianjin Key Lab of BME Measurement, Tianjin University, Tianjin 300072, China.
authors.forename: Yu
authors.initials: Y
authors.lastname: Guo
authors.affiliation: Tianjin Key Lab of BME Measurement, Tianjin University, Tianjin 300072, China ; Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China.
authors.forename: Yuanming
authors.initials: Y
authors.lastname: Feng
authors.affiliation: Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China.
authors.forename: Jian
authors.initials: J
authors.lastname: Sun
authors.affiliation: Tianjin Key Lab of BME Measurement, Tianjin University, Tianjin 300072, China.
authors.forename: Ning
authors.identifier: 0000-0002-1559-111X
authors.initials: N
authors.lastname: Zhang
authors.affiliation: Tianjin Key Lab of BME Measurement, Tianjin University, Tianjin 300072, China.
authors.forename: Wang
authors.initials: W
authors.lastname: Lin
authors.affiliation: Tianjin Key Lab of BME Measurement, Tianjin University, Tianjin 300072, China.
authors.forename: Yu
authors.initials: Y
authors.lastname: Sa
authors.affiliation: Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China.
authors.forename: Ping
authors.initials: P
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D002289:Carcinoma, Non-Small-Cell Lung / Q000175:diagnosis* / Q000000981:diagnostic imaging; D045744:Cell Line, Tumor; D017143:Fuzzy Logic; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging; D008390:Markov Chains; D015233:Models, Statistical; D064847:Multimodal Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated; D049268:Positron-Emission Tomography / Q000379:methods*; D011336:Probability; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D012984:Software; D014057:Tomography, X-Ray Computed / Q000379:methods*