279 related articles for article (PubMed ID: 25842017)
1. Role of the Quantitative Imaging Biomarker Alliance in optimizing CT for the evaluation of lung cancer screen-detected nodules.
Mulshine JL; Gierada DS; Armato SG; Avila RS; Yankelevitz DF; Kazerooni EA; McNitt-Gray MF; Buckler AJ; Sullivan DC
J Am Coll Radiol; 2015 Apr; 12(4):390-5. PubMed ID: 25842017
[TBL] [Abstract][Full Text] [Related]
2. Pulmonary nodules with ground-glass opacity can be reliably measured with low-dose techniques regardless of iterative reconstruction: results of a phantom study.
Siegelman JW; Supanich MP; Gavrielides MA
AJR Am J Roentgenol; 2015 Jun; 204(6):1242-7. PubMed ID: 26001234
[TBL] [Abstract][Full Text] [Related]
3. Statistical analysis of lung nodule volume measurements with CT in a large-scale phantom study.
Li Q; Gavrielides MA; Sahiner B; Myers KJ; Zeng R; Petrick N
Med Phys; 2015 Jul; 42(7):3932-47. PubMed ID: 26133594
[TBL] [Abstract][Full Text] [Related]
4. A new computationally efficient CAD system for pulmonary nodule detection in CT imagery.
Messay T; Hardie RC; Rogers SK
Med Image Anal; 2010 Jun; 14(3):390-406. PubMed ID: 20346728
[TBL] [Abstract][Full Text] [Related]
5. Accuracy of the CT numbers of simulated lung nodules imaged with multi-detector CT scanners.
Goodsitt MM; Chan HP; Way TW; Larson SC; Christodoulou EG; Kim J
Med Phys; 2006 Aug; 33(8):3006-17. PubMed ID: 16964879
[TBL] [Abstract][Full Text] [Related]
6. Pulmonary nodules: a quantitative method of diagnosis by evaluating nodule perimeter difference to approximate oval using three-dimensional CT images.
Kamiya H; Murayama S; Kakinohana Y; Miyara T
Clin Imaging; 2011; 35(2):123-6. PubMed ID: 21377050
[TBL] [Abstract][Full Text] [Related]
7. Three-dimensional volumetric assessment with thoracic CT: a reliable approach for noncalcified lung nodules?
Mazzei MA; Scialpi M; Mazzei FG; Giacobone G; Volterrani L
Radiology; 2010 Feb; 254(2):634; author reply 635. PubMed ID: 20093537
[No Abstract] [Full Text] [Related]
8. Algorithm Variability in the Estimation of Lung Nodule Volume From Phantom CT Scans: Results of the QIBA 3A Public Challenge.
Athelogou M; Kim HJ; Dima A; Obuchowski N; Peskin A; Gavrielides MA; Petrick N; Saiprasad G; Colditz Colditz D; Beaumont H; Oubel E; Tan Y; Zhao B; Kuhnigk JM; Moltz JH; Orieux G; Gillies RJ; Gu Y; Mantri N; Goldmacher G; Zhang L; Vega E; Bloom M; Jarecha R; Soza G; Tietjen C; Takeguchi T; Yamagata H; Peterson S; Masoud O; Buckler AJ
Acad Radiol; 2016 Aug; 23(8):940-52. PubMed ID: 27215408
[TBL] [Abstract][Full Text] [Related]
9. Information-theoretic approach for analyzing bias and variance in lung nodule size estimation with CT: a phantom study.
Gavrielides MA; Zeng R; Kinnard LM; Myers KJ; Petrick N
IEEE Trans Med Imaging; 2010 Oct; 29(10):1795-807. PubMed ID: 20562039
[TBL] [Abstract][Full Text] [Related]
10. In vivo repeatability of automated volume calculations of small pulmonary nodules with CT.
Rampinelli C; De Fiori E; Raimondi S; Veronesi G; Bellomi M
AJR Am J Roentgenol; 2009 Jun; 192(6):1657-61. PubMed ID: 19457831
[TBL] [Abstract][Full Text] [Related]
11. A novel lung nodules detection scheme based on vessel segmentation on CT images.
Jia T; Zhang H; Meng H
Biomed Mater Eng; 2014; 24(6):3179-86. PubMed ID: 25227026
[TBL] [Abstract][Full Text] [Related]
12. Lung cancer screening with CT: evaluation of radiologists and different computer assisted detection software (CAD) as first and second readers for lung nodule detection at different dose levels.
Christe A; Leidolt L; Huber A; Steiger P; Szucs-Farkas Z; Roos JE; Heverhagen JT; Ebner L
Eur J Radiol; 2013 Dec; 82(12):e873-8. PubMed ID: 24074648
[TBL] [Abstract][Full Text] [Related]
13. Automated detection of lung nodules in CT images using shape-based genetic algorithm.
Dehmeshki J; Ye X; Lin X; Valdivieso M; Amin H
Comput Med Imaging Graph; 2007 Sep; 31(6):408-17. PubMed ID: 17524617
[TBL] [Abstract][Full Text] [Related]
14. Modeling of the lung nodules for detection in LDCT scans.
Farag A; Elhabian S; Graham J; Farag A; Elshazly S; Falk R; Mahdi H; Abdelmunim H; Al-Ghaafary S
Annu Int Conf IEEE Eng Med Biol Soc; 2010; 2010():3618-21. PubMed ID: 21096845
[TBL] [Abstract][Full Text] [Related]
15. Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the Lung Image Database Consortium and Image Database Resource Initiative dataset.
Messay T; Hardie RC; Tuinstra TR
Med Image Anal; 2015 May; 22(1):48-62. PubMed ID: 25791434
[TBL] [Abstract][Full Text] [Related]
16. Influence of radiation dose and iterative reconstruction algorithms for measurement accuracy and reproducibility of pulmonary nodule volumetry: A phantom study.
Kim H; Park CM; Song YS; Lee SM; Goo JM
Eur J Radiol; 2014 May; 83(5):848-57. PubMed ID: 24572380
[TBL] [Abstract][Full Text] [Related]
17. Solid, part-solid, or non-solid?: classification of pulmonary nodules in low-dose chest computed tomography by a computer-aided diagnosis system.
Jacobs C; van Rikxoort EM; Scholten ET; de Jong PA; Prokop M; Schaefer-Prokop C; van Ginneken B
Invest Radiol; 2015 Mar; 50(3):168-73. PubMed ID: 25478740
[TBL] [Abstract][Full Text] [Related]
18. Benefit of overlapping reconstruction for improving the quantitative assessment of CT lung nodule volume.
Gavrielides MA; Zeng R; Myers KJ; Sahiner B; Petrick N
Acad Radiol; 2013 Feb; 20(2):173-80. PubMed ID: 23085408
[TBL] [Abstract][Full Text] [Related]
19. Computer-aided differentiation of malignant from benign solitary pulmonary nodules imaged by high-resolution CT.
Iwano S; Nakamura T; Kamioka Y; Ikeda M; Ishigaki T
Comput Med Imaging Graph; 2008 Jul; 32(5):416-22. PubMed ID: 18501556
[TBL] [Abstract][Full Text] [Related]
20. A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification.
Murphy K; van Ginneken B; Schilham AM; de Hoop BJ; Gietema HA; Prokop M
Med Image Anal; 2009 Oct; 13(5):757-70. PubMed ID: 19646913
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
