267 related articles for article (PubMed ID: 37944431)
21. A novel 3D segmentation approach for extracting retinal layers from optical coherence tomography images.
Sleman AA; Soliman A; Elsharkawy M; Giridharan G; Ghazal M; Sandhu H; Schaal S; Keynton R; Elmaghraby A; El-Baz A
Med Phys; 2021 Apr; 48(4):1584-1595. PubMed ID: 33450073
[TBL] [Abstract][Full Text] [Related]
22. Hybrid dual mean-teacher network with double-uncertainty guidance for semi-supervised segmentation of magnetic resonance images.
Zhu J; Bolsterlee B; Chow BVY; Song Y; Meijering E
Comput Med Imaging Graph; 2024 Jul; 115():102383. PubMed ID: 38643551
[TBL] [Abstract][Full Text] [Related]
23. Automated geographic atrophy segmentation for SD-OCT images based on two-stage learning model.
Xu R; Niu S; Chen Q; Ji Z; Rubin D; Chen Y
Comput Biol Med; 2019 Feb; 105():102-111. PubMed ID: 30605812
[TBL] [Abstract][Full Text] [Related]
24. Semi-supervised learning for automatic segmentation of the knee from MRI with convolutional neural networks.
Burton W; Myers C; Rullkoetter P
Comput Methods Programs Biomed; 2020 Jun; 189():105328. PubMed ID: 31958580
[TBL] [Abstract][Full Text] [Related]
25. Neural Networks Application for Accurate Retina Vessel Segmentation from OCT Fundus Reconstruction.
Marciniak T; Stankiewicz A; Zaradzki P
Sensors (Basel); 2023 Feb; 23(4):. PubMed ID: 36850467
[TBL] [Abstract][Full Text] [Related]
26. Deep learning architectures analysis for age-related macular degeneration segmentation on optical coherence tomography scans.
Alsaih K; Yusoff MZ; Tang TB; Faye I; Mériaudeau F
Comput Methods Programs Biomed; 2020 Oct; 195():105566. PubMed ID: 32504911
[TBL] [Abstract][Full Text] [Related]
27. Efficient Combination of CNN and Transformer for Dual-Teacher Uncertainty-guided Semi-supervised Medical Image Segmentation.
Xiao Z; Su Y; Deng Z; Zhang W
Comput Methods Programs Biomed; 2022 Nov; 226():107099. PubMed ID: 36116398
[TBL] [Abstract][Full Text] [Related]
28. Volumetric quantification of choroid and Haller's sublayer using OCT scans: An accurate and unified approach based on stratified smoothing.
Ibrahim MN; Bashar SB; Rasheed MA; Selvam A; Sant V; Sahel JA; Chhablani J; Vupparaboina KK; Jana S
Comput Med Imaging Graph; 2022 Jul; 99():102086. PubMed ID: 35717830
[TBL] [Abstract][Full Text] [Related]
29. Self-Guided Optimization Semi-Supervised Method for Joint Segmentation of Macular Hole and Cystoid Macular Edema in Retinal OCT Images.
Wang M; Lin T; Peng Y; Zhu W; Zhou Y; Shi F; Yu K; Meng Q; Liu Y; Chen Z; Shen Y; Xiang D; Chen H; Chen X
IEEE Trans Biomed Eng; 2023 Jul; 70(7):2013-2024. PubMed ID: 37018248
[TBL] [Abstract][Full Text] [Related]
30. Deep Ensemble Learning Based Objective Grading of Macular Edema by Extracting Clinically Significant Findings from Fused Retinal Imaging Modalities.
Hassan B; Hassan T; Li B; Ahmed R; Hassan O
Sensors (Basel); 2019 Jul; 19(13):. PubMed ID: 31284442
[TBL] [Abstract][Full Text] [Related]
31. Semi-Supervised Capsule cGAN for Speckle Noise Reduction in Retinal OCT Images.
Wang M; Zhu W; Yu K; Chen Z; Shi F; Zhou Y; Ma Y; Peng Y; Bao D; Feng S; Ye L; Xiang D; Chen X
IEEE Trans Med Imaging; 2021 Apr; 40(4):1168-1183. PubMed ID: 33395391
[TBL] [Abstract][Full Text] [Related]
32. Deep Learning-Based Retinal Nerve Fiber Layer Thickness Measurement of Murine Eyes.
Ma R; Liu Y; Tao Y; Alawa KA; Shyu ML; Lee RK
Transl Vis Sci Technol; 2021 Jul; 10(8):21. PubMed ID: 34297789
[TBL] [Abstract][Full Text] [Related]
33. Multiple surface segmentation using convolution neural nets: application to retinal layer segmentation in OCT images.
Shah A; Zhou L; Abrámoff MD; Wu X
Biomed Opt Express; 2018 Sep; 9(9):4509-4526. PubMed ID: 30615698
[TBL] [Abstract][Full Text] [Related]
34. Deep ensemble learning for automated non-advanced AMD classification using optimized retinal layer segmentation and SD-OCT scans.
Moradi M; Chen Y; Du X; Seddon JM
Comput Biol Med; 2023 Mar; 154():106512. PubMed ID: 36701964
[TBL] [Abstract][Full Text] [Related]
35. Stitched vision transformer for age-related macular degeneration detection using retinal optical coherence tomography images.
Azizi MM; Abhari S; Sajedi H
PLoS One; 2024; 19(6):e0304943. PubMed ID: 38837967
[TBL] [Abstract][Full Text] [Related]
36. Deep-learning based multiclass retinal fluid segmentation and detection in optical coherence tomography images using a fully convolutional neural network.
Lu D; Heisler M; Lee S; Ding GW; Navajas E; Sarunic MV; Beg MF
Med Image Anal; 2019 May; 54():100-110. PubMed ID: 30856455
[TBL] [Abstract][Full Text] [Related]
37. Automatic segmentation of OCT retinal boundaries using recurrent neural networks and graph search.
Kugelman J; Alonso-Caneiro D; Read SA; Vincent SJ; Collins MJ
Biomed Opt Express; 2018 Nov; 9(11):5759-5777. PubMed ID: 30460160
[TBL] [Abstract][Full Text] [Related]
38. Multiscale dual attention mechanism for fluid segmentation of optical coherence tomography images.
Chen M; Ma W; Shi L; Li M; Wang C; Zheng G
Appl Opt; 2021 Aug; 60(23):6761-6768. PubMed ID: 34613154
[TBL] [Abstract][Full Text] [Related]
39. UD-MIL: Uncertainty-Driven Deep Multiple Instance Learning for OCT Image Classification.
Wang X; Tang F; Chen H; Luo L; Tang Z; Ran AR; Cheung CY; Heng PA
IEEE J Biomed Health Inform; 2020 Dec; 24(12):3431-3442. PubMed ID: 32248132
[TBL] [Abstract][Full Text] [Related]
40. Automated Segmentation of Retinal Fluid Volumes From Structural and Angiographic Optical Coherence Tomography Using Deep Learning.
Guo Y; Hormel TT; Xiong H; Wang J; Hwang TS; Jia Y
Transl Vis Sci Technol; 2020 Oct; 9(2):54. PubMed ID: 33110708
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]