caa a22 4500 463178718 CHVBK 20180406164832.0 cr unu---uuuuu 170326e20071201xx s 000 0 eng 10.1007/s10278-006-1037-2 doi (NATIONALLICENCE)springer-10.1007/s10278-006-1037-2 Extraction of Metastatic Lymph Nodes from MR Images Using Two Deformable Model-based Approaches [Elektronische Daten] [Jia-Yin Zhou, Wen Fang, Kap-Luk Chan, Vincent Chong, James Khoo] We presented and evaluated two deformable model-based approaches, region plus contour deformation (RPCD), and level sets to extract metastatic cervical nodal lesions from pretreatment T2-weighted magnetic resonance images. The RPCD method first uses a region deformation to achieve a rough boundary of the target node from a manually drawn initial contour, based on signal statistics. After that, an active contour deformation is employed to drive the rough boundary to the real node-normal tissue interface. Differently, the level sets move a manually drawn initial contour toward the desired nodal boundary under the control of the evolvement speed function, which is influenced by image gradient force. The two methods were tested by extracting 33 metastatic cervical nodes from 18 nasopharyngeal carcinoma patients. Experiments on a basis of pixel matching to reference standard showed that RPCD and level sets achieved averaged percentage matching at 82-84% and 87-88%, respectively. In addition, both methods had significantly lower interoperator variances than the manual tracing method. It was suggested these two methods could be useful tools for the evaluation of metastatic nodal volume as an indicator of classification and treatment response, or be alternatives for the delineation of metastatic nodal lesions in radiation treatment planning. SCAR (Society for Computer Applications in Radiology), 2007 Object extraction nationallicence magnetic resonance imaging nationallicence deformable model nationallicence level sets nationallicence nasopharyngeal carcinoma nationallicence metastatic cervical nodes nationallicence Zhou Jia-Yin School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore, Singapore aut Fang Wen School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore aut Chan Kap-Luk School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore, Singapore aut Chong Vincent Department of Diagnostic Radiology, National University of Singapore, Singapore, Singapore aut Khoo James Department of Oncologic Imaging, National Cancer Centre, Singapore, Singapore aut Journal of Digital Imaging Springer-Verlag; http://www.springer-ny.com 20/4(2007-12-01), 336-346 0897-1889 20:4<336 2007 20 10278 https://doi.org/10.1007/s10278-006-1037-2 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10278-006-1037-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Zhou Jia-Yin School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore, Singapore aut NATIONALLICENCE 700 1- Fang Wen School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore aut NATIONALLICENCE 700 1- Chan Kap-Luk School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore, Singapore aut NATIONALLICENCE 700 1- Chong Vincent Department of Diagnostic Radiology, National University of Singapore, Singapore, Singapore aut NATIONALLICENCE 700 1- Khoo James Department of Oncologic Imaging, National Cancer Centre, Singapore, Singapore aut NATIONALLICENCE 773 0- Journal of Digital Imaging Springer-Verlag; http://www.springer-ny.com 20/4(2007-12-01), 336-346 0897-1889 20:4<336 2007 20 10278 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer