121 related articles for article (PubMed ID: 30871684)
1. Active contour algorithm with discriminant analysis for delineating tumors in positron emission tomography.
Comelli A; Stefano A; Bignardi S; Russo G; Sabini MG; Ippolito M; Barone S; Yezzi A
Artif Intell Med; 2019 Mar; 94():67-78. PubMed ID: 30871684
[TBL] [Abstract][Full Text] [Related]
2. A smart and operator independent system to delineate tumours in Positron Emission Tomography scans.
Comelli A; Stefano A; Russo G; Sabini MG; Ippolito M; Bignardi S; Petrucci G; Yezzi A
Comput Biol Med; 2018 Nov; 102():1-15. PubMed ID: 30219733
[TBL] [Abstract][Full Text] [Related]
3. Fully 3D Active Surface with Machine Learning for PET Image Segmentation.
Comelli A
J Imaging; 2020 Oct; 6(11):. PubMed ID: 34460557
[TBL] [Abstract][Full Text] [Related]
4. Development of a new fully three-dimensional methodology for tumours delineation in functional images.
Comelli A; Bignardi S; Stefano A; Russo G; Sabini MG; Ippolito M; Yezzi A
Comput Biol Med; 2020 May; 120():103701. PubMed ID: 32217282
[TBL] [Abstract][Full Text] [Related]
5. Generic and robust method for automatic segmentation of PET images using an active contour model.
Zhuang M; Dierckx RA; Zaidi H
Med Phys; 2016 Aug; 43(8):4483. PubMed ID: 27487865
[TBL] [Abstract][Full Text] [Related]
6. Feasibility of a semi-automated contrast-oriented algorithm for tumor segmentation in retrospectively gated PET images: phantom and clinical validation.
Carles M; Fechter T; Nemer U; Nanko N; Mix M; Nestle U; Schaefer A
Phys Med Biol; 2015 Dec; 60(24):9227-51. PubMed ID: 26576926
[TBL] [Abstract][Full Text] [Related]
7. An enhanced random walk algorithm for delineation of head and neck cancers in PET studies.
Stefano A; Vitabile S; Russo G; Ippolito M; Sabini MG; Sardina D; Gambino O; Pirrone R; Ardizzone E; Gilardi MC
Med Biol Eng Comput; 2017 Jun; 55(6):897-908. PubMed ID: 27638108
[TBL] [Abstract][Full Text] [Related]
8. Evaluation of advanced automatic PET segmentation methods using nonspherical thin-wall inserts.
Berthon B; Marshall C; Evans M; Spezi E
Med Phys; 2014 Feb; 41(2):022502. PubMed ID: 24506646
[TBL] [Abstract][Full Text] [Related]
9. 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. PubMed ID: 27147360
[TBL] [Abstract][Full Text] [Related]
10. Background based Gaussian mixture model lesion segmentation in PET.
Soffientini CD; De Bernardi E; Zito F; Castellani M; Baselli G
Med Phys; 2016 May; 43(5):2662. PubMed ID: 27147375
[TBL] [Abstract][Full Text] [Related]
11. Tumor delineation using PET in head and neck cancers: threshold contouring and lesion volumes.
Ford EC; Kinahan PE; Hanlon L; Alessio A; Rajendran J; Schwartz DL; Phillips M
Med Phys; 2006 Nov; 33(11):4280-8. PubMed ID: 17153406
[TBL] [Abstract][Full Text] [Related]
12. ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography.
Berthon B; Marshall C; Evans M; Spezi E
Phys Med Biol; 2016 Jul; 61(13):4855-69. PubMed ID: 27273293
[TBL] [Abstract][Full Text] [Related]
13. A Novel Framework for Automated Segmentation and Labeling of Homogeneous Versus Heterogeneous Lung Tumors in [
Soufi M; Kamali-Asl A; Geramifar P; Rahmim A
Mol Imaging Biol; 2017 Jun; 19(3):456-468. PubMed ID: 27770402
[TBL] [Abstract][Full Text] [Related]
14. Rapid Contour-based Segmentation for
Besson FL; Henry T; Meyer C; Chevance V; Roblot V; Blanchet E; Arnould V; Grimon G; Chekroun M; Mabille L; Parent F; Seferian A; Bulifon S; Montani D; Humbert M; Chaumet-Riffaud P; Lebon V; Durand E
Radiology; 2018 Jul; 288(1):277-284. PubMed ID: 29613842
[TBL] [Abstract][Full Text] [Related]
15. Contourlet-based active contour model for PET image segmentation.
Abdoli M; Dierckx RA; Zaidi H
Med Phys; 2013 Aug; 40(8):082507. PubMed ID: 23927352
[TBL] [Abstract][Full Text] [Related]
16. A fully automatic approach for multimodal PET and MR image segmentation in gamma knife treatment planning.
Rundo L; Stefano A; Militello C; Russo G; Sabini MG; D'Arrigo C; Marletta F; Ippolito M; Mauri G; Vitabile S; Gilardi MC
Comput Methods Programs Biomed; 2017 Jun; 144():77-96. PubMed ID: 28495008
[TBL] [Abstract][Full Text] [Related]
17. Segmentation-free direct tumor volume and metabolic activity estimation from PET scans.
Taghanaki SA; Duggan N; Ma H; Hou X; Celler A; Benard F; Hamarneh G
Comput Med Imaging Graph; 2018 Jan; 63():52-66. PubMed ID: 29336922
[TBL] [Abstract][Full Text] [Related]
18. An innovative iterative thresholding algorithm for tumour segmentation and volumetric quantification on SPECT images: Monte Carlo-based methodology and validation.
Pacilio M; Basile C; Shcherbinin S; Caselli F; Ventroni G; Aragno D; Mango L; Santini E
Med Phys; 2011 Jun; 38(6):3050-61. PubMed ID: 21815378
[TBL] [Abstract][Full Text] [Related]
19. The use of zeolites to generate PET phantoms for the validation of quantification strategies in oncology.
Zito F; De Bernardi E; Soffientini C; Canzi C; Casati R; Gerundini P; Baselli G
Med Phys; 2012 Sep; 39(9):5353-61. PubMed ID: 22957603
[TBL] [Abstract][Full Text] [Related]
20. Comparison of three image segmentation techniques for target volume delineation in positron emission tomography.
Drever LA; Roa W; McEwan A; Robinson D
J Appl Clin Med Phys; 2007 Mar; 8(2):93-109. PubMed ID: 17592458
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
