Format
Sort by
Items per page

Send to

Choose Destination

Links from PubMed

Items: 1 to 20 of 79

1.

Centerline extraction with principal curve tracing to improve 3D level set esophagus segmentation in CT images.

Kurugol S, Bas E, Erdogmus D, Dy JG, Sharp GC, Brooks DH.

Conf Proc IEEE Eng Med Biol Soc. 2011;2011:3403-6. doi: 10.1109/IEMBS.2011.6090921.

2.

Locally Deformable Shape Model to Improve 3D Level Set based Esophagus Segmentation.

Kurugol S, Ozay N, Dy JG, Sharp GC, Brooks DH.

Proc IAPR Int Conf Pattern Recogn. 2010 Aug 23:3955-3958.

3.

Esophagus segmentation from 3D CT data using skeleton prior-based graph cut.

Grosgeorge D, Petitjean C, Dubray B, Ruan S.

Comput Math Methods Med. 2013;2013:547897. doi: 10.1155/2013/547897. Epub 2013 Aug 29.

4.

Model-based esophagus segmentation from CT scans using a spatial probability map.

Feulner J, Zhou SK, Huber M, Cavallaro A, Hornegger J, Comaniciu D.

Med Image Comput Comput Assist Interv. 2010;13(Pt 1):95-102.

PMID:
20879219
5.

Esophagus segmentation in CT via 3D fully convolutional neural network and random walk.

Fechter T, Adebahr S, Baltas D, Ben Ayed I, Desrosiers C, Dolz J.

Med Phys. 2017 Dec;44(12):6341-6352. doi: 10.1002/mp.12593. Epub 2017 Oct 23.

PMID:
28940372
6.

Object-constrained meshless deformable algorithm for high speed 3D nonrigid registration between CT and CBCT.

Chen T, Kim S, Goyal S, Jabbour S, Zhou J, Rajagopal G, Haffty B, Yue N.

Med Phys. 2010 Jan;37(1):197-210.

PMID:
20175482
7.

A Dirichlet process mixture model for automatic (18)F-FDG PET image segmentation: Validation study on phantoms and on lung and esophageal lesions.

Giri MG, Cavedon C, Mazzarotto R, Ferdeghini M.

Med Phys. 2016 May;43(5):2491. doi: 10.1118/1.4947123.

PMID:
27147360
8.

Segmentation of the thoracic aorta in noncontrast cardiac CT images.

Avila-Montes OC, Kurkure U, Nakazato R, Berman DS, Dey D, Kakadiaris IA.

IEEE J Biomed Health Inform. 2013 Sep;17(5):936-49. doi: 10.1109/JBHI.2013.2269292.

PMID:
25055373
9.

Fast automatic segmentation of the esophagus from 3D CT data using a probabilistic model.

Feulner J, Zhou SK, Cavallaro A, Seifert S, Hornegger J, Comaniciu D.

Med Image Comput Comput Assist Interv. 2009;12(Pt 1):255-62.

PMID:
20425995
10.

Random Walk and Graph Cut for Co-Segmentation of Lung Tumor on PET-CT Images.

Ju W, Xiang D, Zhang B, Wang L, Kopriva I, Chen X.

IEEE Trans Image Process. 2015 Dec;24(12):5854-67. doi: 10.1109/TIP.2015.2488902. Epub 2015 Oct 8. Erratum in: IEEE Trans Image Process. 2016 Mar;25(3):1192. Xiang, Deihui [corrected to Xiang, Dehui].

PMID:
26462198
11.

A 3D global-to-local deformable mesh model based registration and anatomy-constrained segmentation method for image guided prostate radiotherapy.

Zhou J, Kim S, Jabbour S, Goyal S, Haffty B, Chen T, Levinson L, Metaxas D, Yue NJ.

Med Phys. 2010 Mar;37(3):1298-308.

PMID:
20384267
12.

Airway Segmentation and Centerline Extraction from Thoracic CT - Comparison of a New Method to State of the Art Commercialized Methods.

Reynisson PJ, Scali M, Smistad E, Hofstad EF, Leira HO, Lindseth F, Nagelhus Hernes TA, Amundsen T, Sorger H, Langø T.

PLoS One. 2015 Dec 11;10(12):e0144282. doi: 10.1371/journal.pone.0144282. eCollection 2015.

13.

A low-interaction automatic 3D liver segmentation method using computed tomography for selective internal radiation therapy.

Goryawala M, Gulec S, Bhatt R, McGoron AJ, Adjouadi M.

Biomed Res Int. 2014;2014:198015. doi: 10.1155/2014/198015. Epub 2014 Jul 3.

14.

Observation of normal appearance and wall thickness of esophagus on CT images.

Xia F, Mao J, Ding J, Yang H.

Eur J Radiol. 2009 Dec;72(3):406-11. doi: 10.1016/j.ejrad.2008.09.002. Epub 2008 Oct 16.

PMID:
18929453
15.

Automatic detection of lung nodules in CT datasets based on stable 3D mass-spring models.

Cascio D, Magro R, Fauci F, Iacomi M, Raso G.

Comput Biol Med. 2012 Nov;42(11):1098-109. doi: 10.1016/j.compbiomed.2012.09.002. Epub 2012 Sep 26.

PMID:
23020972
16.

Abdominal multi-organ segmentation from CT images using conditional shape-location and unsupervised intensity priors.

Okada T, Linguraru MG, Hori M, Summers RM, Tomiyama N, Sato Y.

Med Image Anal. 2015 Dec;26(1):1-18. doi: 10.1016/j.media.2015.06.009. Epub 2015 Jul 4.

17.

Bayesian segmentation of human facial tissue using 3D MR-CT information fusion, resolution enhancement and partial volume modelling.

Şener E, Mumcuoglu EU, Hamcan S.

Comput Methods Programs Biomed. 2016 Feb;124:31-44. doi: 10.1016/j.cmpb.2015.10.009. Epub 2015 Oct 23.

PMID:
26574298
18.

A probabilistic model for automatic segmentation of the esophagus in 3-D CT scans.

Feulner J, Zhou SK, Hammon M, Seifert S, Huber M, Comaniciu D, Hornegger J, Cavallaro A.

IEEE Trans Med Imaging. 2011 Jun;30(6):1252-64. doi: 10.1109/TMI.2011.2112372. Epub 2011 Feb 7.

PMID:
21303741
19.

Toward accurate tooth segmentation from computed tomography images using a hybrid level set model.

Gan Y, Xia Z, Xiong J, Zhao Q, Hu Y, Zhang J.

Med Phys. 2015 Jan;42(1):14-27. doi: 10.1118/1.4901521.

PMID:
25563244
20.

Computer-aided measurement of liver volumes in CT by means of geodesic active contour segmentation coupled with level-set algorithms.

Suzuki K, Kohlbrenner R, Epstein ML, Obajuluwa AM, Xu J, Hori M.

Med Phys. 2010 May;37(5):2159-66.

Supplemental Content

Support Center