A review on segmentation of positron emission tomography images.
Positron Emission Tomography (PET), a non-invasive functional imaging method at the molecular level, images the distribution of biologically targeted radiotracers with high sensitivity. PET imaging provides detailed quantitative information about many diseases and is often used to evaluate inflammation, infection, and cancer by detecting emitted photons from a radiotracer localized to abnormal cells. In order to differentiate abnormal tissue from surrounding areas in PET images, image segmentation methods play a vital role; therefore, accurate image segmentation is often necessary for proper disease detection, diagnosis, treatment planning, and follow-ups. In this review paper, we present state-of-the-art PET image segmentation methods, as well as the recent advances in image segmentation techniques. In order to make this manuscript self-contained, we also briefly explain the fundamentals of PET imaging, the challenges of diagnostic PET image analysis, and the effects of these challenges on the segmentation results.
Center for Infectious Disease Imaging, Department of Radiology and Imaging Sciences, National Institutes of Health (NIH), Bethesda, MD 20892, United States.
Brent
B
Foster
Center for Infectious Disease Imaging, Department of Radiology and Imaging Sciences, National Institutes of Health (NIH), Bethesda, MD 20892, United States. Electronic address: ulas.bagci@nih.gov.
Ulas
U
Bagci
Center for Infectious Disease Imaging, Department of Radiology and Imaging Sciences, National Institutes of Health (NIH), Bethesda, MD 20892, United States.
Awais
A
Mansoor
Center for Infectious Disease Imaging, Department of Radiology and Imaging Sciences, National Institutes of Health (NIH), Bethesda, MD 20892, United States.
Ziyue
Z
Xu
Center for Infectious Disease Imaging, Department of Radiology and Imaging Sciences, National Institutes of Health (NIH), Bethesda, MD 20892, United States.
Daniel J
DJ
Mollura
D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D016000:Cluster Analysis; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007249:Inflammation / Q000175:diagnosis / Q000000981:diagnostic imaging; D008279:Magnetic Resonance Imaging / Q000379:methods; D064847:Multimodal Imaging / Q000379:methods; D009369:Neoplasms / Q000175:diagnosis / Q000000981:diagnostic imaging; D016011:Normal Distribution; D049268:Positron-Emission Tomography / Q000379:methods*; D019275:Radiopharmaceuticals / Q000008:administration & dosage; D012984:Software; D013269:Stochastic Processes; D014057:Tomography, X-Ray Computed / Q000379:methods
D019275:Radiopharmaceuticals
Image segmentation; MRI-PET; PET; PET-CT; Review; SUV; Thresholding