179 related articles for article (PubMed ID: 37448544)
1. Automatic Choroidal Segmentation in Optical Coherence Tomography Images Based on Curvelet Transform and Graph Theory.
Eghtedar RA; Esmaeili M; Peyman A; Akhlaghi M; Rasta SH
J Med Signals Sens; 2023; 13(2):92-100. PubMed ID: 37448544
[TBL] [Abstract][Full Text] [Related]
2. Three-dimensional Segmentation of Retinal Cysts from Spectral-domain Optical Coherence Tomography Images by the Use of Three-dimensional Curvelet Based K-SVD.
Esmaeili M; Dehnavi AM; Rabbani H; Hajizadeh F
J Med Signals Sens; 2016; 6(3):166-71. PubMed ID: 27563573
[TBL] [Abstract][Full Text] [Related]
3. Automatic segmentation of choroidal thickness in optical coherence tomography.
Alonso-Caneiro D; Read SA; Collins MJ
Biomed Opt Express; 2013; 4(12):2795-812. PubMed ID: 24409381
[TBL] [Abstract][Full Text] [Related]
4. An Open-Source Deep Learning Algorithm for Efficient and Fully Automatic Analysis of the Choroid in Optical Coherence Tomography.
Burke J; Engelmann J; Hamid C; Reid-Schachter M; Pearson T; Pugh D; Dhaun N; Storkey A; King S; MacGillivray TJ; Bernabeu MO; MacCormick IJC
Transl Vis Sci Technol; 2023 Nov; 12(11):27. PubMed ID: 37988073
[TBL] [Abstract][Full Text] [Related]
5. Speckle Noise Reduction in Optical Coherence Tomography Using Two-dimensional Curvelet-based Dictionary Learning.
Esmaeili M; Dehnavi AM; Rabbani H; Hajizadeh F
J Med Signals Sens; 2017; 7(2):86-91. PubMed ID: 28553581
[TBL] [Abstract][Full Text] [Related]
6. Automatic segmentation of layers in chorio-retinal complex using Graph-based method for ultra-speed 1.7 MHz wide field swept source FDML optical coherence tomography.
Poddar R; Shukla V; Alam Z; Mohan M
Med Biol Eng Comput; 2024 May; 62(5):1375-1393. PubMed ID: 38191981
[TBL] [Abstract][Full Text] [Related]
7. Three-dimensional curvelet-based dictionary learning for speckle noise removal of optical coherence tomography.
Esmaeili M; Dehnavi AM; Hajizadeh F; Rabbani H
Biomed Opt Express; 2020 Feb; 11(2):586-608. PubMed ID: 32133216
[TBL] [Abstract][Full Text] [Related]
8. An Update on Choroidal Layer Segmentation Methods in Optical Coherence Tomography Images: a Review.
Alizadeh Eghtedar R; Esmaeili M; Peyman A; Akhlaghi M; Rasta SH
J Biomed Phys Eng; 2022 Feb; 12(1):1-20. PubMed ID: 35155288
[TBL] [Abstract][Full Text] [Related]
9. Automatic choroidal segmentation in OCT images using supervised deep learning methods.
Kugelman J; Alonso-Caneiro D; Read SA; Hamwood J; Vincent SJ; Chen FK; Collins MJ
Sci Rep; 2019 Sep; 9(1):13298. PubMed ID: 31527630
[TBL] [Abstract][Full Text] [Related]
10. An automated method for choroidal thickness measurement from Enhanced Depth Imaging Optical Coherence Tomography images.
Hussain MA; Bhuiyan A; Ishikawa H; Theodore Smith R; Schuman JS; Kotagiri R
Comput Med Imaging Graph; 2018 Jan; 63():41-51. PubMed ID: 29366655
[TBL] [Abstract][Full Text] [Related]
11. Automated segmentation of the choroid in retinal optical coherence tomography images.
Lu H; Boonarpha N; Kwong MT; Zheng Y
Annu Int Conf IEEE Eng Med Biol Soc; 2013; 2013():5869-72. PubMed ID: 24111074
[TBL] [Abstract][Full Text] [Related]
12. CHOROIDAL NEOVASCULAR AREA AND VESSEL DENSITY COMPARISON BETWEEN TWO SWEPT-SOURCE OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY DEVICES.
Ohayon A; Sacconi R; Semoun O; Corbelli E; Souied EH; Querques G
Retina; 2020 Mar; 40(3):521-528. PubMed ID: 30589664
[TBL] [Abstract][Full Text] [Related]
13. Choroidal thickness maps from spectral domain and swept source optical coherence tomography: algorithmic versus ground truth annotation.
Philip AM; Gerendas BS; Zhang L; Faatz H; Podkowinski D; Bogunovic H; Abramoff MD; Hagmann M; Leitner R; Simader C; Sonka M; Waldstein SM; Schmidt-Erfurth U
Br J Ophthalmol; 2016 Oct; 100(10):1372-6. PubMed ID: 26769670
[TBL] [Abstract][Full Text] [Related]
14. Segmentation of the geographic atrophy in spectral-domain optical coherence tomography and fundus autofluorescence images.
Hu Z; Medioni GG; Hernandez M; Hariri A; Wu X; Sadda SR
Invest Ophthalmol Vis Sci; 2013 Dec; 54(13):8375-83. PubMed ID: 24265015
[TBL] [Abstract][Full Text] [Related]
15. Semi-supervised contrast learning-based segmentation of choroidal vessel in optical coherence tomography images.
Liu X; Pan J; Zhang Y; Li X; Tang J
Phys Med Biol; 2023 Dec; 68(24):. PubMed ID: 37972415
[No Abstract] [Full Text] [Related]
16. Automatic segmentation of the choroid in enhanced depth imaging optical coherence tomography images.
Tian J; Marziliano P; Baskaran M; Tun TA; Aung T
Biomed Opt Express; 2013 Mar; 4(3):397-411. PubMed ID: 23504041
[TBL] [Abstract][Full Text] [Related]
17. OCT Retinal and Choroidal Layer Instance Segmentation Using Mask R-CNN.
Viedma IA; Alonso-Caneiro D; Read SA; Collins MJ
Sensors (Basel); 2022 Mar; 22(5):. PubMed ID: 35271165
[TBL] [Abstract][Full Text] [Related]
18. Deep Neural Network Regression for Automated Retinal Layer Segmentation in Optical Coherence Tomography Images.
Ngo L; Cha J; Han JH
IEEE Trans Image Process; 2019 Aug; ():. PubMed ID: 31395546
[TBL] [Abstract][Full Text] [Related]
19. Automated segmentation of choroidal layers from 3-dimensional macular optical coherence tomography scans.
Lee K; Warren AK; Abràmoff MD; Wahle A; Whitmore SS; Han IC; Fingert JH; Scheetz TE; Mullins RF; Sonka M; Sohn EH
J Neurosci Methods; 2021 Aug; 360():109267. PubMed ID: 34157370
[TBL] [Abstract][Full Text] [Related]
20. Joint Multimodal Deep Learning-based Automatic Segmentation of Indocyanine Green Angiography and OCT Images for Assessment of Polypoidal Choroidal Vasculopathy Biomarkers.
Loo J; Teo KYC; Vyas CH; Jordan-Yu JMN; Juhari AB; Jaffe GJ; Cheung CMG; Farsiu S
Ophthalmol Sci; 2023 Sep; 3(3):100292. PubMed ID: 37025946
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
