1207 related articles for article (PubMed ID: 30501859)
1. A novel retinal vessel detection approach based on multiple deep convolution neural networks.
Guo Y; Budak Ü; Şengür A
Comput Methods Programs Biomed; 2018 Dec; 167():43-48. PubMed ID: 30501859
[TBL] [Abstract][Full Text] [Related]
2. Scale-space approximated convolutional neural networks for retinal vessel segmentation.
Noh KJ; Park SJ; Lee S
Comput Methods Programs Biomed; 2019 Sep; 178():237-246. PubMed ID: 31416552
[TBL] [Abstract][Full Text] [Related]
3. Artery vein classification in fundus images using serially connected U-Nets.
Karlsson RA; Hardarson SH
Comput Methods Programs Biomed; 2022 Apr; 216():106650. PubMed ID: 35139461
[TBL] [Abstract][Full Text] [Related]
4. Improving dense conditional random field for retinal vessel segmentation by discriminative feature learning and thin-vessel enhancement.
Zhou L; Yu Q; Xu X; Gu Y; Yang J
Comput Methods Programs Biomed; 2017 Sep; 148():13-25. PubMed ID: 28774435
[TBL] [Abstract][Full Text] [Related]
5. A new deep learning method for blood vessel segmentation in retinal images based on convolutional kernels and modified U-Net model.
Gegundez-Arias ME; Marin-Santos D; Perez-Borrero I; Vasallo-Vazquez MJ
Comput Methods Programs Biomed; 2021 Jun; 205():106081. PubMed ID: 33882418
[TBL] [Abstract][Full Text] [Related]
6. Retinal vascular junction detection and classification via deep neural networks.
Zhao H; Sun Y; Li H
Comput Methods Programs Biomed; 2020 Jan; 183():105096. PubMed ID: 31586789
[TBL] [Abstract][Full Text] [Related]
7. Joint segmentation and classification of retinal arteries/veins from fundus images.
Girard F; Kavalec C; Cheriet F
Artif Intell Med; 2019 Mar; 94():96-109. PubMed ID: 30871687
[TBL] [Abstract][Full Text] [Related]
8. BSEResU-Net: An attention-based before-activation residual U-Net for retinal vessel segmentation.
Li D; Rahardja S
Comput Methods Programs Biomed; 2021 Jun; 205():106070. PubMed ID: 33857703
[TBL] [Abstract][Full Text] [Related]
9. NFN＋: A novel network followed network for retinal vessel segmentation.
Wu Y; Xia Y; Song Y; Zhang Y; Cai W
Neural Netw; 2020 Jun; 126():153-162. PubMed ID: 32222424
[TBL] [Abstract][Full Text] [Related]
10. Development of automatic retinal vessel segmentation method in fundus images via convolutional neural networks.
Joonyoung Song ; Boreom Lee
Annu Int Conf IEEE Eng Med Biol Soc; 2017 Jul; 2017():681-684. PubMed ID: 29059964
[TBL] [Abstract][Full Text] [Related]
11. A new robust method for blood vessel segmentation in retinal fundus images based on weighted line detector and hidden Markov model.
Zhou C; Zhang X; Chen H
Comput Methods Programs Biomed; 2020 Apr; 187():105231. PubMed ID: 31786454
[TBL] [Abstract][Full Text] [Related]
12. DCCMED-Net: Densely connected and concatenated multi Encoder-Decoder CNNs for retinal vessel extraction from fundus images.
Budak Ü; Cömert Z; Çıbuk M; Şengür A
Med Hypotheses; 2020 Jan; 134():109426. PubMed ID: 31622926
[TBL] [Abstract][Full Text] [Related]
13. Robust Hidden Markov Model based intelligent blood vessel detection of fundus images.
Hassan M; Amin M; Murtza I; Khan A; Chaudhry A
Comput Methods Programs Biomed; 2017 Nov; 151():193-201. PubMed ID: 28947001
[TBL] [Abstract][Full Text] [Related]
14. Developing a Novel Methodology by Integrating Deep Learning and HMM for Segmentation of Retinal Blood Vessels in Fundus Images.
Hassan M; Ali S; Kim JY; Saadia A; Sanaullah M; Alquhayz H; Safdar K
Interdiscip Sci; 2023 Jun; 15(2):273-292. PubMed ID: 36611082
[TBL] [Abstract][Full Text] [Related]
15. DAVS-NET: Dense Aggregation Vessel Segmentation Network for retinal vasculature detection in fundus images.
Raza M; Naveed K; Akram A; Salem N; Afaq A; Madni HA; Khan MAU; Din MZ
PLoS One; 2021; 16(12):e0261698. PubMed ID: 34972109
[TBL] [Abstract][Full Text] [Related]
16. Fast and efficient retinal blood vessel segmentation method based on deep learning network.
Boudegga H; Elloumi Y; Akil M; Hedi Bedoui M; Kachouri R; Abdallah AB
Comput Med Imaging Graph; 2021 Jun; 90():101902. PubMed ID: 33892389
[TBL] [Abstract][Full Text] [Related]
17. Multi-level deep supervised networks for retinal vessel segmentation.
Mo J; Zhang L
Int J Comput Assist Radiol Surg; 2017 Dec; 12(12):2181-2193. PubMed ID: 28577175
[TBL] [Abstract][Full Text] [Related]
18. BTS-DSN: Deeply supervised neural network with short connections for retinal vessel segmentation.
Guo S; Wang K; Kang H; Zhang Y; Gao Y; Li T
Int J Med Inform; 2019 Jun; 126():105-113. PubMed ID: 31029251
[TBL] [Abstract][Full Text] [Related]
19. Contrast Enhancement of RGB Retinal Fundus Images for Improved Segmentation of Blood Vessels Using Convolutional Neural Networks.
Sule O; Viriri S
J Digit Imaging; 2023 Apr; 36(2):414-432. PubMed ID: 36456839
[TBL] [Abstract][Full Text] [Related]
20. Deep learning-based detection and classification of geographic atrophy using a deep convolutional neural network classifier.
Treder M; Lauermann JL; Eter N
Graefes Arch Clin Exp Ophthalmol; 2018 Nov; 256(11):2053-2060. PubMed ID: 30091055
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
