title: Joint segmentation of anatomical and functional images: applications in quantification of lesions from PET, PET-CT, MRI-PET, and MRI-PET-CT images.
abstract: We present a novel method for the joint segmentation of anatomical and functional images. Our proposed methodology unifies the domains of anatomical and functional images, represents them in a product lattice, and performs simultaneous delineation of regions based on random walk image segmentation. Furthermore, we also propose a simple yet effective object/background seed localization method to make the proposed segmentation process fully automatic. Our study uses PET, PET-CT, MRI-PET, and fused MRI-PET-CT scans (77 studies in all) from 56 patients who had various lesions in different body regions. We validated the effectiveness of the proposed method on different PET phantoms as well as on clinical images with respect to the ground truth segmentation provided by clinicians. Experimental results indicate that the presented method is superior to threshold and Bayesian methods commonly used in PET image segmentation, is more accurate and robust compared to the other PET-CT segmentation methods recently published in the literature, and also it is general in the sense of simultaneously segmenting multiple scans in real-time with high accuracy needed in routine clinical use.
authors.affiliation: Center for Infectious Diseases Imaging, National Institutes of Health, Bethesda, MD, United States; Department of Radiology and Imaging Sciences, National Institutes of Health, Bethesda, MD, United States. Electronic address: ulasbagci@gmail.com.
authors.forename: Ulas
authors.initials: U
authors.lastname: Bagci
authors.forename: Jayaram K
authors.initials: JK
authors.lastname: Udupa
authors.forename: Neil
authors.initials: N
authors.lastname: Mendhiratta
authors.forename: Brent
authors.initials: B
authors.lastname: Foster
authors.forename: Ziyue
authors.initials: Z
authors.lastname: Xu
authors.forename: Jianhua
authors.initials: J
authors.lastname: Yao
authors.forename: Xinjian
authors.initials: X
authors.lastname: Chen
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Mollura
mesh_terms: D000465:Algorithms; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D064847:Multimodal Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D049268:Positron-Emission Tomography / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: MRI-PET Co-segmentation; PET segmentation; PET-CT Co-segmentation; Random Walk; Simultaneous segmentation