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Items: 1 to 20 of 661

1.

Automatic segmentation of thoracic and pelvic CT images for radiotherapy planning using implicit anatomic knowledge and organ-specific segmentation strategies.

Haas B, Coradi T, Scholz M, Kunz P, Huber M, Oppitz U, André L, Lengkeek V, Huyskens D, van Esch A, Reddick R.

Phys Med Biol. 2008 Mar 21;53(6):1751-71. doi: 10.1088/0031-9155/53/6/017. Epub 2008 Mar 7.

PMID:
18367801
2.

Multi-institutional quantitative evaluation and clinical validation of Smart Probabilistic Image Contouring Engine (SPICE) autosegmentation of target structures and normal tissues on computer tomography images in the head and neck, thorax, liver, and male pelvis areas.

Zhu M, Bzdusek K, Brink C, Eriksen JG, Hansen O, Jensen HA, Gay HA, Thorstad W, Widder J, Brouwer CL, Steenbakkers RJ, Vanhauten HA, Cao JQ, McBrayne G, Patel SH, Cannon DM, Hardcastle N, Tomé WA, Guckenberg M, Parikh PJ.

Int J Radiat Oncol Biol Phys. 2013 Nov 15;87(4):809-16. doi: 10.1016/j.ijrobp.2013.08.007.

PMID:
24138920
3.

A qualitative and a quantitative analysis of an auto-segmentation module for prostate cancer.

Huyskens DP, Maingon P, Vanuytsel L, Remouchamps V, Roques T, Dubray B, Haas B, Kunz P, Coradi T, Bühlman R, Reddick R, Esch AV, Salamon E.

Radiother Oncol. 2009 Mar;90(3):337-45. doi: 10.1016/j.radonc.2008.08.007. Epub 2008 Sep 21.

PMID:
18812252
4.
5.

Automatic segmentation of anatomical structures from CT scans of thorax for RTP.

Özsavaş EE, Telatar Z, Dirican B, Sağer Ö, Beyzadeoğlu M.

Comput Math Methods Med. 2014;2014:472890. doi: 10.1155/2014/472890. Epub 2014 Dec 18.

6.

Automatic bladder segmentation on CBCT for multiple plan ART of bladder cancer using a patient-specific bladder model.

Chai X, van Herk M, Betgen A, Hulshof M, Bel A.

Phys Med Biol. 2012 Jun 21;57(12):3945-62. doi: 10.1088/0031-9155/57/12/3945. Epub 2012 May 30.

PMID:
22643320
7.

Augmenting atlas-based liver segmentation for radiotherapy treatment planning by incorporating image features proximal to the atlas contours.

Li D, Liu L, Chen J, Li H, Yin Y, Ibragimov B, Xing L.

Phys Med Biol. 2017 Jan 7;62(1):272-288. Epub 2016 Dec 17.

PMID:
27991439
8.

Clinical evaluation of multi-atlas based segmentation of lymph node regions in head and neck and prostate cancer patients.

Sjöberg C, Lundmark M, Granberg C, Johansson S, Ahnesjö A, Montelius A.

Radiat Oncol. 2013 Oct 3;8:229. doi: 10.1186/1748-717X-8-229.

9.

Automated model-based organ delineation for radiotherapy planning in prostatic region.

Pekar V, McNutt TR, Kaus MR.

Int J Radiat Oncol Biol Phys. 2004 Nov 1;60(3):973-80.

PMID:
15465216
10.
11.

First performance evaluation of software for automatic segmentation, labeling and reformation of anatomical aligned axial images of the thoracolumbar spine at CT.

Scholtz JE, Wichmann JL, Kaup M, Fischer S, Kerl JM, Lehnert T, Vogl TJ, Bauer RW.

Eur J Radiol. 2015 Mar;84(3):437-442. doi: 10.1016/j.ejrad.2014.11.043. Epub 2014 Dec 13.

PMID:
25554009
12.
13.

A deformable-model approach to semi-automatic segmentation of CT images demonstrated by application to the spinal canal.

Burnett SS, Starkschalla G, Stevens CW, Liao Z.

Med Phys. 2004 Feb;31(2):251-63.

PMID:
15000611
14.

Performance validation of deformable image registration in the pelvic region.

Zambrano V, Furtado H, Fabri D, Lütgendorf-Caucig C, Góra J, Stock M, Mayer R, Birkfellner W, Georg D.

J Radiat Res. 2013 Jul;54 Suppl 1:i120-8. doi: 10.1093/jrr/rrt045.

15.

Learning image context for segmentation of the prostate in CT-guided radiotherapy.

Li W, Liao S, Feng Q, Chen W, Shen D.

Phys Med Biol. 2012 Mar 7;57(5):1283-308. doi: 10.1088/0031-9155/57/5/1283. Epub 2012 Feb 17.

16.

Systematic evaluation of three different commercial software solutions for automatic segmentation for adaptive therapy in head-and-neck, prostate and pleural cancer.

La Macchia M, Fellin F, Amichetti M, Cianchetti M, Gianolini S, Paola V, Lomax AJ, Widesott L.

Radiat Oncol. 2012 Sep 18;7:160. doi: 10.1186/1748-717X-7-160.

17.

Automatic segmentation of head and neck CT images for radiotherapy treatment planning using multiple atlases, statistical appearance models, and geodesic active contours.

Fritscher KD, Peroni M, Zaffino P, Spadea MF, Schubert R, Sharp G.

Med Phys. 2014 May;41(5):051910. doi: 10.1118/1.4871623.

18.

Automatic thoracic anatomy segmentation on CT images using hierarchical fuzzy models and registration.

Sun K, Udupa JK, Odhner D, Tong Y, Zhao L, Torigian DA.

Med Phys. 2016 Mar;43(3):1487-500. doi: 10.1118/1.4942486.

PMID:
26936732
19.

Volumetric visualization of anatomy for treatment planning.

Pelizzari SA, Grzeszczuk R, Chen GT, Heimann R, Haraf DJ, Vijayakumar S, Ryan MJ.

Int J Radiat Oncol Biol Phys. 1996 Jan 1;34(1):205-11.

PMID:
12118552
20.

The utility of atlas-assisted segmentation in the male pelvis is dependent on the interobserver agreement of the structures segmented.

Langmack KA, Perry C, Sinstead C, Mills J, Saunders D.

Br J Radiol. 2014 Nov;87(1043):20140299. doi: 10.1259/bjr.20140299. Epub 2014 Aug 29.

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