A Review on Segmentation of Positron Emission Tomography Images
By Brent Foster, Ulas Bagci, Awais Mansoor, Ziyue Xu, Daniel J. Mollura. Comput. Biol. Medicine, 2014
386 citations recorded by the source corpus.
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.
Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/3e1cb5ef832157d4c3c2cbd04e88f323e4527126
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Citations in the corpus
- State-of-the-Art Methods for Brain Tissue Segmentation: A Review (2017, background): “track contours and surface [3], [15]–[23].”
- Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation (2020, neutral): “Nevertheless, the most widely used is SUV, which can also be used in SPECT [348] .”