A review on segmentation of positron emission tomography images

被引:276
|
作者
Foster, Brent [1 ]
Bagci, Ulas [1 ]
Mansoor, Awais [1 ]
Xu, Ziyue [1 ]
Mollura, Daniel J. [1 ]
机构
[1] NIH, Ctr Infect Dis Imaging, Dept Radiol & Imaging Sci, Bethesda, MD 20892 USA
基金
美国国家卫生研究院;
关键词
Image segmentation; PET; SUV; Thresholding; PET-CT; MRI-PET; Review; CELL LUNG-CANCER; GROSS TUMOR VOLUME; STANDARDIZED UPTAKE VALUES; DYNAMIC PET IMAGES; GAUSSIAN MIXTURE MODEL; GRADIENT-BASED METHOD; FDG-PET; F-18-FDG PET; CT IMAGES; INTEROBSERVER VARIABILITY;
D O I
10.1016/j.compbiomed.2014.04.014
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
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. Published by Elsevier Ltd.
引用
收藏
页码:76 / 96
页数:21
相关论文
共 50 条
  • [1] Block iterative methods for Bayesian segmentation of positron emission tomography images
    Velipasaoglu, EO
    Ersoy, OK
    [J]. INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, PROCEEDINGS - VOL II, 1996, : 269 - 272
  • [2] Intensity threshold based solid tumour segmentation method for Positron Emission Tomography (PET) images: A review
    Tamal, Mahbubunnabi
    [J]. HELIYON, 2020, 6 (10)
  • [3] Concurrent Segmentation and Estimation of Transmission Images for Attenuation Correction in Positron Emission Tomography
    Anderson, John M. M.
    Kim, Yoon-Chul
    Votaw, John R.
    [J]. IEEE TRANSACTIONS ON NUCLEAR SCIENCE, 2009, 56 (01) : 136 - 146
  • [4] Deep learning model with collage images for the segmentation of dedicated breast positron emission tomography images
    Imokawa, Tomoki
    Satoh, Yoko
    Fujioka, Tomoyuki
    Takahashi, Kanae
    Mori, Mio
    Kubota, Kazunori
    Onishi, Hiroshi
    Tateishi, Ukihide
    [J]. BREAST CANCER, 2023,
  • [5] Segmentation of positron emission tomography images: Some recommendations for target delineation in radiation oncology
    Lee, John A.
    [J]. RADIOTHERAPY AND ONCOLOGY, 2010, 96 (03) : 302 - 307
  • [6] Global Thresholding Technique for Basal Ganglia Segmentation from Positron Emission Tomography Images
    Maalej, Zainab
    Ben Rejab, Fahmi
    Nouira, Kaouther
    [J]. SOFT COMPUTING AND ITS ENGINEERING APPLICATIONS, ICSOFTCOMP 2022, 2023, 1788 : 84 - 95
  • [7] Positron emission tomography and positron emission tomography/computerized tomography of urological malignancies: An update review
    Bouchelouche, Kirsten
    Oehr, Peter
    [J]. JOURNAL OF UROLOGY, 2008, 179 (01): : 34 - 45
  • [8] Noise components on positron emission tomography images
    Geng, JH
    Chen, YM
    Yin, DY
    Tian, JH
    Chen, SZ
    [J]. BIO-MEDICAL MATERIALS AND ENGINEERING, 2003, 13 (02) : 181 - 186
  • [9] Robust Segmentation and Accurate Target Definition for Positron Emission Tomography Images Using Affinity Propagation
    Foster, Brent
    Bagci, Ulas
    Luna, Brian
    Dey, Bappaditya
    Bishai, William
    Jain, Sanjay
    Xu, Ziyue
    Mollura, Daniel J.
    [J]. 2013 IEEE 10TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI), 2013, : 1461 - 1464
  • [10] Comparative Study of Segmentation Techniques for Basal Ganglia Detection Based on Positron Emission Tomography Images
    Maalej Z.
    Ben Rejab F.
    Nouira K.
    [J]. SN Computer Science, 5 (4)