Literature DB >> 12860465

Segmentation and visualization of nasopharyngeal carcinoma using MRI.

Jiayin Zhou1, Tuan-Kay Lim, Vincent Chong, Jing Huang.   

Abstract

In this study, a semi-automatic system was developed for nasopharyngeal carcinoma (NPC) tumor segmentation, volume measurement and visualization using magnetic resonance imaging (MRI). Some novel algorithms for tumor segmentation from MRI and inter-slice interpolation were integrated in this medical diagnosis system. This system was applied to 10 MR image data sets of NPC patients and satisfactory results were achieved. This system can be used as a clinical image analysis tool for doctors or radiologists to obtain tumor location from MRI, tumor volume estimation, and 3D information.

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Year:  2003        PMID: 12860465     DOI: 10.1016/s0010-4825(03)00018-0

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  7 in total

1.  The relationship between nasopharyngeal carcinoma tumor volume and TNM T-classification: a quantitative analysis.

Authors:  Jia-Yin Zhou; Vincent F H Chong; James B K Khoo; Kap-Luk Chan; Jing Huang
Journal:  Eur Arch Otorhinolaryngol       Date:  2006-09-21       Impact factor: 2.503

2.  Region-based nasopharyngeal carcinoma lesion segmentation from MRI using clustering- and classification-based methods with learning.

Authors:  Wei Huang; Kap Luk Chan; Jiayin Zhou
Journal:  J Digit Imaging       Date:  2013-06       Impact factor: 4.056

3.  Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing.

Authors:  Thomas M Hsieh; Yi-Min Liu; Chun-Chih Liao; Furen Xiao; I-Jen Chiang; Jau-Min Wong
Journal:  BMC Med Inform Decis Mak       Date:  2011-08-26       Impact factor: 2.796

4.  Unidimensional measurement may be superior to assess primary tumor response after neoadjuvant chemotherapy for nasopharyngeal carcinoma.

Authors:  Chuanben Chen; Xiurong Lin; Yuanji Xu; Penggang Bai; Youping Xiao; Yuhui Pan; Chao Li; Zhizhong Lin; Mingwei Zhang; Yunbin Chen
Journal:  Oncotarget       Date:  2017-07-18

5.  Fully Automated Delineation of Gross Tumor Volume for Head and Neck Cancer on PET-CT Using Deep Learning: A Dual-Center Study.

Authors:  Bin Huang; Zhewei Chen; Po-Man Wu; Yufeng Ye; Shi-Ting Feng; Ching-Yee Oliver Wong; Liyun Zheng; Yong Liu; Tianfu Wang; Qiaoliang Li; Bingsheng Huang
Journal:  Contrast Media Mol Imaging       Date:  2018-10-24       Impact factor: 3.161

6.  Tumor Segmentation in Contrast-Enhanced Magnetic Resonance Imaging for Nasopharyngeal Carcinoma: Deep Learning with Convolutional Neural Network.

Authors:  Qiaoliang Li; Yuzhen Xu; Zhewei Chen; Dexiang Liu; Shi-Ting Feng; Martin Law; Yufeng Ye; Bingsheng Huang
Journal:  Biomed Res Int       Date:  2018-10-17       Impact factor: 3.411

7.  Automated nasopharyngeal carcinoma segmentation in magnetic resonance images by combination of convolutional neural networks and graph cut.

Authors:  Zongqing Ma; Xi Wu; Qi Song; Yong Luo; Yan Wang; Jiliu Zhou
Journal:  Exp Ther Med       Date:  2018-07-18       Impact factor: 2.447

  7 in total

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