149 related articles for article (PubMed ID: 29993573)
1. Corneal Endothelial Cell Segmentation by Classifier-Driven Merging of Oversegmented Images.
Vigueras-Guillen JP; Andrinopoulou ER; Engel A; Lemij HG; van Rooij J; Vermeer KA; van Vliet LJ
IEEE Trans Med Imaging; 2018 Oct; 37(10):2278-2289. PubMed ID: 29993573
[TBL] [Abstract][Full Text] [Related]
2. Influence of applied corneal endothelium image segmentation techniques on the clinical parameters.
Piorkowski A; Nurzynska K; Gronkowska-Serafin J; Selig B; Boldak C; Reska D
Comput Med Imaging Graph; 2017 Jan; 55():13-27. PubMed ID: 27553657
[TBL] [Abstract][Full Text] [Related]
3. Development of a Reliable Automated Algorithm for the Morphometric Analysis of Human Corneal Endothelium.
Scarpa F; Ruggeri A
Cornea; 2016 Sep; 35(9):1222-8. PubMed ID: 27310881
[TBL] [Abstract][Full Text] [Related]
4. Fully automatic evaluation of the corneal endothelium from in vivo confocal microscopy.
Selig B; Vermeer KA; Rieger B; Hillenaar T; Luengo Hendriks CL
BMC Med Imaging; 2015 Apr; 15():13. PubMed ID: 25928199
[TBL] [Abstract][Full Text] [Related]
5. Morphometry of organ cultured corneal endothelium using Voronoi segmentation.
Brookes NH
Cell Tissue Bank; 2017 Jun; 18(2):167-183. PubMed ID: 28374155
[TBL] [Abstract][Full Text] [Related]
6. Fully convolutional architecture vs sliding-window CNN for corneal endothelium cell segmentation.
Vigueras-Guillén JP; Sari B; Goes SF; Lemij HG; van Rooij J; Vermeer KA; van Vliet LJ
BMC Biomed Eng; 2019; 1():4. PubMed ID: 32903308
[TBL] [Abstract][Full Text] [Related]
7. A fully automated cell segmentation and morphometric parameter system for quantifying corneal endothelial cell morphology.
Al-Fahdawi S; Qahwaji R; Al-Waisy AS; Ipson S; Ferdousi M; Malik RA; Brahma A
Comput Methods Programs Biomed; 2018 Jul; 160():11-23. PubMed ID: 29728238
[TBL] [Abstract][Full Text] [Related]
8. A system for the automatic estimation of morphometric parameters of corneal endothelium in alizarine red-stained images.
Ruggeri A; Scarpa F; De Luca M; Meltendorf C; Schroeter J
Br J Ophthalmol; 2010 May; 94(5):643-7. PubMed ID: 20447967
[TBL] [Abstract][Full Text] [Related]
9. Comparison of endothelial cell count using confocal and contact specular microscopy.
Klais CM; Bühren J; Kohnen T
Ophthalmologica; 2003; 217(2):99-103. PubMed ID: 12592045
[TBL] [Abstract][Full Text] [Related]
10. Automated morphometric description of human corneal endothelium from in-vivo specular and confocal microscopy.
Scarpa F; Ruggeri A
Annu Int Conf IEEE Eng Med Biol Soc; 2016 Aug; 2016():1296-1299. PubMed ID: 28268563
[TBL] [Abstract][Full Text] [Related]
11. An automatic approach for cell detection and segmentation of corneal endothelium in specular microscope.
Karmakar R; Nooshabadi S; Eghrari A
Graefes Arch Clin Exp Ophthalmol; 2022 Apr; 260(4):1215-1224. PubMed ID: 34741660
[TBL] [Abstract][Full Text] [Related]
12. Automated segmentation of the corneal endothelium in a large set of 'real-world' specular microscopy images using the U-Net architecture.
Daniel MC; Atzrodt L; Bucher F; Wacker K; Böhringer S; Reinhard T; Böhringer D
Sci Rep; 2019 Mar; 9(1):4752. PubMed ID: 30894636
[TBL] [Abstract][Full Text] [Related]
13. Endothelial cells analysis with the TOPCON specular microscope SP-2000P and IMAGEnet system.
Cheung SW; Cho P
Curr Eye Res; 2000 Oct; 21(4):788-98. PubMed ID: 11120569
[TBL] [Abstract][Full Text] [Related]
14. Unbiased corneal tissue analysis using Gabor-domain optical coherence microscopy and machine learning for automatic segmentation of corneal endothelial cells.
Canavesi C; Cogliati A; Hindman HB
J Biomed Opt; 2020 Aug; 25(9):1-17. PubMed ID: 32770867
[TBL] [Abstract][Full Text] [Related]
15. Segmentation of corneal endothelium images using a U-Net-based convolutional neural network.
Fabijańska A
Artif Intell Med; 2018 Jun; 88():1-13. PubMed ID: 29680687
[TBL] [Abstract][Full Text] [Related]
16. Comparison of Noncontact Specular and Confocal Microscopy for Evaluation of Corneal Endothelium.
Huang J; Maram J; Tepelus TC; Sadda SR; Chopra V; Lee OL
Eye Contact Lens; 2018 Sep; 44 Suppl 1():S144-S150. PubMed ID: 28346276
[TBL] [Abstract][Full Text] [Related]
17. Evaluation of repeatability for the automatic estimation of endothelial cell density in donor corneas.
Ruggeri A; Grisan E; Schroeter J
Br J Ophthalmol; 2007 Sep; 91(9):1213-5. PubMed ID: 17431018
[TBL] [Abstract][Full Text] [Related]
18. In vivo three-dimensional reconstruction of the cornea from confocal microscopy images.
Scarpa F; Fiorin D; Ruggeri A
Annu Int Conf IEEE Eng Med Biol Soc; 2007; 2007():747-50. PubMed ID: 18002064
[TBL] [Abstract][Full Text] [Related]
19. DenseUNets with feedback non-local attention for the segmentation of specular microscopy images of the corneal endothelium with guttae.
Vigueras-Guillén JP; van Rooij J; van Dooren BTH; Lemij HG; Islamaj E; van Vliet LJ; Vermeer KA
Sci Rep; 2022 Aug; 12(1):14035. PubMed ID: 35982194
[TBL] [Abstract][Full Text] [Related]
20. Segmentation of endothelial cells of the cornea from the distance map of confocal microscope images.
Herrera-Pereda R; Crispi AT; Babin D; Philips W
Comput Biol Med; 2021 Dec; 139():104953. PubMed ID: 34735943
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
