These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

169 related articles for article (PubMed ID: 33843422)

  • 41. Blood vessel segmentation in color fundus images based on regional and Hessian features.
    Shah SAA; Tang TB; Faye I; Laude A
    Graefes Arch Clin Exp Ophthalmol; 2017 Aug; 255(8):1525-1533. PubMed ID: 28474130
    [TBL] [Abstract][Full Text] [Related]  

  • 42. Blood Vessel Segmentation of Fundus Images by Major Vessel Extraction and Subimage Classification.
    Roychowdhury S; Koozekanani DD; Parhi KK
    IEEE J Biomed Health Inform; 2015 May; 19(3):1118-28. PubMed ID: 25014980
    [TBL] [Abstract][Full Text] [Related]  

  • 43. SUD-GAN: Deep Convolution Generative Adversarial Network Combined with Short Connection and Dense Block for Retinal Vessel Segmentation.
    Yang T; Wu T; Li L; Zhu C
    J Digit Imaging; 2020 Aug; 33(4):946-957. PubMed ID: 32323089
    [TBL] [Abstract][Full Text] [Related]  

  • 44. Fréchet PDF based Matched Filter Approach for Retinal Blood Vessels Segmentation.
    Saroj SK; Kumar R; Singh NP
    Comput Methods Programs Biomed; 2020 Oct; 194():105490. PubMed ID: 32504830
    [TBL] [Abstract][Full Text] [Related]  

  • 45. A cognitive deep learning approach for medical image processing.
    Fakhouri HN; Alawadi S; Awaysheh FM; Alkhabbas F; Zraqou J
    Sci Rep; 2024 Feb; 14(1):4539. PubMed ID: 38402321
    [TBL] [Abstract][Full Text] [Related]  

  • 46. Retina Image Vessel Segmentation Using a Hybrid CGLI Level Set Method.
    Chen G; Chen M; Li J; Zhang E
    Biomed Res Int; 2017; 2017():1263056. PubMed ID: 28840122
    [TBL] [Abstract][Full Text] [Related]  

  • 47. TUnet-LBF: Retinal fundus image fine segmentation model based on transformer Unet network and LBF.
    Zhang H; Ni W; Luo Y; Feng Y; Song R; Wang X
    Comput Biol Med; 2023 Jun; 159():106937. PubMed ID: 37084640
    [TBL] [Abstract][Full Text] [Related]  

  • 48. Retinal blood vessel segmentation based on Densely Connected U-Net.
    Cheng YL; Ma MN; Zhang LJ; Jin CJ; Ma L; Zhou Y
    Math Biosci Eng; 2020 Apr; 17(4):3088-3108. PubMed ID: 32987518
    [TBL] [Abstract][Full Text] [Related]  

  • 49. [Automatic detection of vessels in color fundus images].
    Jiménez S; Alemany P; Fondón I; Foncubierta A; Acha B; Serrano C
    Arch Soc Esp Oftalmol; 2010 Mar; 85(3):103-9. PubMed ID: 20619121
    [TBL] [Abstract][Full Text] [Related]  

  • 50. Retinal vessel segmentation method based on RSP-SA Unet network.
    Sun K; Chen Y; Dong F; Wu Q; Geng J; Chen Y
    Med Biol Eng Comput; 2024 Feb; 62(2):605-620. PubMed ID: 37964177
    [TBL] [Abstract][Full Text] [Related]  

  • 51. Atrous residual convolutional neural network based on U-Net for retinal vessel segmentation.
    Wu J; Liu Y; Zhu Y; Li Z
    PLoS One; 2022; 17(8):e0273318. PubMed ID: 35994494
    [TBL] [Abstract][Full Text] [Related]  

  • 52. HT-Net: A Hybrid Transformer Network for Fundus Vessel Segmentation.
    Hu X; Wang L; Li Y
    Sensors (Basel); 2022 Sep; 22(18):. PubMed ID: 36146132
    [TBL] [Abstract][Full Text] [Related]  

  • 53. 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]  

  • 54. A Hybrid Unsupervised Approach for Retinal Vessel Segmentation.
    Khan KB; Siddique MS; Ahmad M; Mazzara M
    Biomed Res Int; 2020; 2020():8365783. PubMed ID: 33381585
    [TBL] [Abstract][Full Text] [Related]  

  • 55. Accurate Retinal Vessel Segmentation in Color Fundus Images via Fully Attention-Based Networks.
    Li K; Qi X; Luo Y; Yao Z; Zhou X; Sun M
    IEEE J Biomed Health Inform; 2021 Jun; 25(6):2071-2081. PubMed ID: 33001809
    [TBL] [Abstract][Full Text] [Related]  

  • 56. 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]  

  • 57. Synthetic OCT-A blood vessel maps using fundus images and generative adversarial networks.
    Coronado I; Pachade S; Trucco E; Abdelkhaleq R; Yan J; Salazar-Marioni S; Jagolino-Cole A; Bahrainian M; Channa R; Sheth SA; Giancardo L
    Sci Rep; 2023 Sep; 13(1):15325. PubMed ID: 37714881
    [TBL] [Abstract][Full Text] [Related]  

  • 58. Stimulus-guided adaptive transformer network for retinal blood vessel segmentation in fundus images.
    Lin J; Huang X; Zhou H; Wang Y; Zhang Q
    Med Image Anal; 2023 Oct; 89():102929. PubMed ID: 37598606
    [TBL] [Abstract][Full Text] [Related]  

  • 59. Multi-path cascaded U-net for vessel segmentation from fundus fluorescein angiography sequential images.
    Sun G; Liu X; Yu X
    Comput Methods Programs Biomed; 2021 Nov; 211():106422. PubMed ID: 34598080
    [TBL] [Abstract][Full Text] [Related]  

  • 60. 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]  

    [Previous]   [Next]    [New Search]
    of 9.