A review on segmentation of positron emission tomography images
Computers in Biology and Medicine, 50, 76–96
Abstract
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.
Abstract sourceThis 2014 review surveys methods for segmenting positron emission tomography (PET) images, outlines challenges that affect segmentation, and discusses approaches for evaluating segmentation results.
Summary
Brent Foster, Ulas Bagci, Awais Mansoor, Ziyue Xu, and Daniel J. Mollura review PET image segmentation methods, including manual, threshold-based, stochastic and learning-based, region-based, boundary-based, and multimodal approaches. The article also describes PET imaging fundamentals and factors that complicate segmentation, such as image noise, limited resolution, motion, and variability in lesion appearance.
The authors discuss evaluation approaches including overlap, sensitivity and specificity, boundary-based measures, and volume comparisons. They caution that volume-based comparisons alone do not fully characterize segmentation accuracy. As a review, the paper synthesizes published methods rather than reporting a new clinical validation study. Read the full text in PubMed Central.
- DOI: 10.1016/j.compbiomed.2014.04.014
- Volume 50, pages 76–96
Cite this work
Brent Foster; Ulas Bagci; Awais Mansoor; Ziyue Xu; Daniel J. Mollura (2014). A review on segmentation of positron emission tomography images. Computers in Biology and Medicine, 50, 76–96 https://doi.org/10.1016/j.compbiomed.2014.04.014
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