Tumor Segmentation on <sup>18</sup>F FDG-PET Images Using Graph Cut and Local Spatial Information

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ISSN Print: 2164-2788
ISSN Online: 2164-2796
www.scirp.org/journal/ojmi
E-mail: ojmi@scirp.org

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"Tumor Segmentation on ¹⁸F FDG-PET Images Using Graph Cut and Local Spatial Information"
written by Mizuho Nishio, Atsushi K. Kono, Kazuhiro Kubo, Hisanobu Koyama, Tatsuya Nishii, Kazuro Sugimura,
published by Open Journal of Medical Imaging, Vol.5 No.3, 2015

has been cited by the following article(s):

[1]	SLIC-supervoxels-based response evaluation of osteosarcoma treated with neoadjuvant chemotherapy using multi-parametric MR imaging
	2020

[2]	Usefulness of gradient tree boosting for predicting histological subtype and EGFR mutation status of non-small cell lung cancer on 18F FDG-PET/CT
	2019

[3]	Fast PET Scan Tumor Segmentation Using Superpixels, Principal Component Analysis and K-Means Clustering
	Methods and Protocols, 2018

[4]	SEGMENTATION STRATEGY OF PET BRAIN TUMOR IMAGE
	2017

[1]	SLIC-supervoxels-based response evaluation of osteosarcoma treated with neoadjuvant chemotherapy using multi-parametric MR imaging European Radiology, 2020 DOI:10.1007/s00330-019-06647-1

[2]	Usefulness of gradient tree boosting for predicting histological subtype and EGFR mutation status of non-small cell lung cancer on 18F FDG-PET/CT Annals of Nuclear Medicine, 2019 DOI:10.1007/s12149-019-01414-0

[3]	Fast PET Scan Tumor Segmentation Using Superpixels, Principal Component Analysis and K-Means Clustering Methods and Protocols, 2018 DOI:10.3390/mps1010007

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