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Journal Abstract Search
307 related items for PubMed ID: 15708280
1. Comparison of human and automatic segmentations of kidneys from CT images. Rao M, Stough J, Chi YY, Muller K, Tracton G, Pizer SM, Chaney EL. Int J Radiat Oncol Biol Phys; 2005 Mar 01; 61(3):954-60. PubMed ID: 15708280 [Abstract] [Full Text] [Related]
2. 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 01; 42(1):14-27. PubMed ID: 25563244 [Abstract] [Full Text] [Related]
3. Generic method for automatic bladder segmentation on cone beam CT using a patient-specific bladder shape model. van de Schoot AJ, Schooneveldt G, Wognum S, Hoogeman MS, Chai X, Stalpers LJ, Rasch CR, Bel A. Med Phys; 2014 Mar 01; 41(3):031707. PubMed ID: 24593711 [Abstract] [Full Text] [Related]
4. Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates. Pipitone J, Park MT, Winterburn J, Lett TA, Lerch JP, Pruessner JC, Lepage M, Voineskos AN, Chakravarty MM, Alzheimer's Disease Neuroimaging Initiative. Neuroimage; 2014 Nov 01; 101():494-512. PubMed ID: 24784800 [Abstract] [Full Text] [Related]
5. 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. PubMed ID: 22643320 [Abstract] [Full Text] [Related]
6. Auto-segmentation of normal and target structures in head and neck CT images: a feature-driven model-based approach. Qazi AA, Pekar V, Kim J, Xie J, Breen SL, Jaffray DA. Med Phys; 2011 Nov 21; 38(11):6160-70. PubMed ID: 22047381 [Abstract] [Full Text] [Related]
7. Deep learning from dual-energy information for whole-heart segmentation in dual-energy and single-energy non-contrast-enhanced cardiac CT. Bruns S, Wolterink JM, Takx RAP, van Hamersvelt RW, Suchá D, Viergever MA, Leiner T, Išgum I. Med Phys; 2020 Oct 21; 47(10):5048-5060. PubMed ID: 32786071 [Abstract] [Full Text] [Related]
8. A semiautomatic segmentation method for prostate in CT images using local texture classification and statistical shape modeling. Shahedi M, Halicek M, Guo R, Zhang G, Schuster DM, Fei B. Med Phys; 2018 Jun 21; 45(6):2527-2541. PubMed ID: 29611216 [Abstract] [Full Text] [Related]
9. PSMA-PET improves deep learning-based automated CT kidney segmentation. Leube J, Horn M, Hartrampf PE, Buck AK, Lassmann M, Tran-Gia J. Z Med Phys; 2024 May 21; 34(2):231-241. PubMed ID: 37666698 [Abstract] [Full Text] [Related]
10. Cavity contour segmentation in chest radiographs using supervised learning and dynamic programming. Maduskar P, Hogeweg L, de Jong PA, Peters-Bax L, Dawson R, Ayles H, Sánchez CI, van Ginneken B. Med Phys; 2014 Jul 21; 41(7):071912. PubMed ID: 24989390 [Abstract] [Full Text] [Related]
11. Shape-intensity prior level set combining probabilistic atlas and probability map constrains for automatic liver segmentation from abdominal CT images. Wang J, Cheng Y, Guo C, Wang Y, Tamura S. Int J Comput Assist Radiol Surg; 2016 May 21; 11(5):817-26. PubMed ID: 26646416 [Abstract] [Full Text] [Related]
12. Semiautomatic bladder segmentation on CBCT using a population-based model for multiple-plan ART of bladder cancer. Chai X, van Herk M, Betgen A, Hulshof M, Bel A. Phys Med Biol; 2012 Dec 21; 57(24):N525-41. PubMed ID: 23190683 [Abstract] [Full Text] [Related]
13. Evaluation of a Semi-automatic Right Ventricle Segmentation Method on Short-Axis MR Images. Yilmaz P, Wallecan K, Kristanto W, Aben JP, Moelker A. J Digit Imaging; 2018 Oct 21; 31(5):670-679. PubMed ID: 29524154 [Abstract] [Full Text] [Related]
14. Whole liver segmentation based on deep learning and manual adjustment for clinical use in SIRT. Tang X, Jafargholi Rangraz E, Coudyzer W, Bertels J, Robben D, Schramm G, Deckers W, Maleux G, Baete K, Verslype C, Gooding MJ, Deroose CM, Nuyts J. Eur J Nucl Med Mol Imaging; 2020 Nov 21; 47(12):2742-2752. PubMed ID: 32314026 [Abstract] [Full Text] [Related]
15. Tissue segmentation of head and neck CT images for treatment planning: a multiatlas approach combined with intensity modeling. Fortunati V, Verhaart RF, van der Lijn F, Niessen WJ, Veenland JF, Paulides MM, van Walsum T. Med Phys; 2013 Jul 21; 40(7):071905. PubMed ID: 23822442 [Abstract] [Full Text] [Related]