167 related articles for article (PubMed ID: 32772385)
21. Automatic detection of multisize pulmonary nodules in CT images: Large-scale validation of the false-positive reduction step.
Gupta A; Saar T; Martens O; Moullec YL
Med Phys; 2018 Mar; 45(3):1135-1149. PubMed ID: 29359462
[TBL] [Abstract][Full Text] [Related]
22. Soft computing approach to 3D lung nodule segmentation in CT.
Badura P; Pietka E
Comput Biol Med; 2014 Oct; 53():230-43. PubMed ID: 25173811
[TBL] [Abstract][Full Text] [Related]
23. 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. PubMed ID: 23020972
[TBL] [Abstract][Full Text] [Related]
24. Optical Flow Methods for Lung Nodule Segmentation on LIDC-IDRI Images.
Suji RJ; Bhadouria SS; Dhar J; Godfrey WW
J Digit Imaging; 2020 Oct; 33(5):1306-1324. PubMed ID: 32556911
[TBL] [Abstract][Full Text] [Related]
25. Automated segmentation refinement of small lung nodules in CT scans by local shape analysis.
Diciotti S; Lombardo S; Falchini M; Picozzi G; Mascalchi M
IEEE Trans Biomed Eng; 2011 Dec; 58(12):3418-28. PubMed ID: 21914567
[TBL] [Abstract][Full Text] [Related]
26. BRISC-an open source pulmonary nodule image retrieval framework.
Lam MO; Disney T; Raicu DS; Furst J; Channin DS
J Digit Imaging; 2007 Nov; 20 Suppl 1(Suppl 1):63-71. PubMed ID: 17701069
[TBL] [Abstract][Full Text] [Related]
27. Nodule-CLIP: Lung nodule classification based on multi-modal contrastive learning.
Sun L; Zhang M; Lu Y; Zhu W; Yi Y; Yan F
Comput Biol Med; 2024 Jun; 175():108505. PubMed ID: 38688129
[TBL] [Abstract][Full Text] [Related]
28. The Lung Image Database Consortium (LIDC): ensuring the integrity of expert-defined "truth".
Armato SG; Roberts RY; McNitt-Gray MF; Meyer CR; Reeves AP; McLennan G; Engelmann RM; Bland PH; Aberle DR; Kazerooni EA; MacMahon H; van Beek EJ; Yankelevitz D; Croft BY; Clarke LP
Acad Radiol; 2007 Dec; 14(12):1455-63. PubMed ID: 18035275
[TBL] [Abstract][Full Text] [Related]
29. 3-D segmentation algorithm of small lung nodules in spiral CT images.
Diciotti S; Picozzi G; Falchini M; Mascalchi M; Villari N; Valli G
IEEE Trans Inf Technol Biomed; 2008 Jan; 12(1):7-19. PubMed ID: 18270032
[TBL] [Abstract][Full Text] [Related]
30. Segmentation of pulmonary nodules in CT images based on 3D-UNET combined with three-dimensional conditional random field optimization.
Wu W; Gao L; Duan H; Huang G; Ye X; Nie S
Med Phys; 2020 Sep; 47(9):4054-4063. PubMed ID: 32428969
[TBL] [Abstract][Full Text] [Related]
31. The Lung Image Database Consortium (LIDC): a comparison of different size metrics for pulmonary nodule measurements.
Reeves AP; Biancardi AM; Apanasovich TV; Meyer CR; MacMahon H; van Beek EJ; Kazerooni EA; Yankelevitz D; McNitt-Gray MF; McLennan G; Armato SG; Henschke CI; Aberle DR; Croft BY; Clarke LP
Acad Radiol; 2007 Dec; 14(12):1475-85. PubMed ID: 18035277
[TBL] [Abstract][Full Text] [Related]
32. A longitudinal four-dimensional computed tomography and cone beam computed tomography dataset for image-guided radiation therapy research in lung cancer.
Hugo GD; Weiss E; Sleeman WC; Balik S; Keall PJ; Lu J; Williamson JF
Med Phys; 2017 Feb; 44(2):762-771. PubMed ID: 27991677
[TBL] [Abstract][Full Text] [Related]
33. Machine learning to predict lung nodule biopsy method using CT image features: A pilot study.
Sumathipala Y; Shafiq M; Bongen E; Brinton C; Paik D
Comput Med Imaging Graph; 2019 Jan; 71():1-8. PubMed ID: 30448741
[TBL] [Abstract][Full Text] [Related]
34. Cascaded-Recalibrated Multiple Instance Deep Model for Pathologic-Level Lung Cancer Prediction in CT Images.
Wang Q; Zhou Y; Huang J; Liu Z; Zhang W; Liu Q; Cheng JZ
Comput Intell Neurosci; 2022; 2022():9469234. PubMed ID: 35733559
[TBL] [Abstract][Full Text] [Related]
35. Mapping LIDC, RadLex™, and lung nodule image features.
Opulencia P; Channin DS; Raicu DS; Furst JD
J Digit Imaging; 2011 Apr; 24(2):256-70. PubMed ID: 20390436
[TBL] [Abstract][Full Text] [Related]
36. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge.
Setio AAA; Traverso A; de Bel T; Berens MSN; Bogaard CVD; Cerello P; Chen H; Dou Q; Fantacci ME; Geurts B; Gugten RV; Heng PA; Jansen B; de Kaste MMJ; Kotov V; Lin JY; Manders JTMC; Sóñora-Mengana A; García-Naranjo JC; Papavasileiou E; Prokop M; Saletta M; Schaefer-Prokop CM; Scholten ET; Scholten L; Snoeren MM; Torres EL; Vandemeulebroucke J; Walasek N; Zuidhof GCA; Ginneken BV; Jacobs C
Med Image Anal; 2017 Dec; 42():1-13. PubMed ID: 28732268
[TBL] [Abstract][Full Text] [Related]
37. 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]
38. Highdicom: a Python Library for Standardized Encoding of Image Annotations and Machine Learning Model Outputs in Pathology and Radiology.
Bridge CP; Gorman C; Pieper S; Doyle SW; Lennerz JK; Kalpathy-Cramer J; Clunie DA; Fedorov AY; Herrmann MD
J Digit Imaging; 2022 Dec; 35(6):1719-1737. PubMed ID: 35995898
[TBL] [Abstract][Full Text] [Related]
39. Improved lung nodule diagnosis accuracy using lung CT images with uncertain class.
Wang Z; Xin J; Sun P; Lin Z; Yao Y; Gao X
Comput Methods Programs Biomed; 2018 Aug; 162():197-209. PubMed ID: 29903487
[TBL] [Abstract][Full Text] [Related]
40. An analysis of early studies released by the Lung Imaging Database Consortium (LIDC).
Ross JC; Miller JV; Turner WD; Kelliher TP
Acad Radiol; 2007 Nov; 14(11):1382-8. PubMed ID: 17964461
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]