Automatic choroidal segmentation in OCT images using supervised deep learning methods
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David Alonso-Caneiro | Jason Kugelman | Scott A Read | Jared Hamwood | Stephen J Vincent | Fred K Chen | Michael J Collins | Scott A. Read | Stephen J. Vincent | F. Chen | M. Collins | S. Read | D. Alonso-Caneiro | J. Kugelman | Jared Hamwood
[1] William J Feuer,et al. An optical coherence tomography-guided, variable dosing regimen with intravitreal ranibizumab (Lucentis) for neovascular age-related macular degeneration. , 2007, American journal of ophthalmology.
[2] Richard F Spaide,et al. Optical coherence tomography: imaging of the choroid and beyond. , 2013, Survey of ophthalmology.
[3] David Alonso-Caneiro,et al. Automatic segmentation of choroidal thickness in optical coherence tomography. , 2013, Biomedical optics express.
[4] Nassir Navab,et al. Recalibrating Fully Convolutional Networks With Spatial and Channel “Squeeze and Excitation” Blocks , 2018, IEEE Transactions on Medical Imaging.
[5] Wolfgang Drexler,et al. Retinal and choroidal thickness in early age-related macular degeneration. , 2011, American journal of ophthalmology.
[6] Peter A. Calabresi,et al. Topology guaranteed segmentation of the human retina from OCT using convolutional neural networks , 2018, ArXiv.
[7] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Thomas Theelen,et al. Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks. , 2017, Biomedical optics express.
[9] Dimitri Palaz,et al. Towards End-to-End Speech Recognition , 2016 .
[10] David Alonso-Caneiro,et al. Posterior Choroidal Stroma Reduces Accuracy of Automated Segmentation of Outer Choroidal Boundary in Swept Source Optical Coherence Tomography. , 2018, Investigative ophthalmology & visual science.
[11] Chao Yang,et al. A Survey on Deep Transfer Learning , 2018, ICANN.
[12] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[13] Min Chen,et al. Automated Segmentation of the Choroid in EDI-OCT Images with Retinal Pathology Using Convolution Neural Networks , 2017, FIFI/OMIA@MICCAI.
[14] Tianqi Chen,et al. Empirical Evaluation of Rectified Activations in Convolutional Network , 2015, ArXiv.
[15] Vikram S Brar,et al. Normative data for macular thickness by high-definition spectral-domain optical coherence tomography (spectralis). , 2009, American journal of ophthalmology.
[16] Zenghui Wang,et al. Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review , 2017, Neural Computation.
[17] C Ross Ethier,et al. Shadow removal and contrast enhancement in optical coherence tomography images of the human optic nerve head. , 2011, Investigative ophthalmology & visual science.
[18] Edsger W. Dijkstra,et al. A note on two problems in connexion with graphs , 1959, Numerische Mathematik.
[19] Timothy Dozat,et al. Incorporating Nesterov Momentum into Adam , 2016 .
[20] Majid A. Al-Taee,et al. A Novel Choroid Segmentation Method for Retinal Diagnosis Using Deep Learning , 2017, 2017 10th International Conference on Developments in eSystems Engineering (DeSE).
[21] David Alonso-Caneiro,et al. Choroidal thickness in childhood. , 2013, Investigative ophthalmology & visual science.
[22] H. Lemij,et al. Depth-resolved model-based reconstruction of attenuation coefficients in optical coherence tomography. , 2013, Biomedical optics express.
[23] C. Costagliola,et al. Enhanced depth imaging spectral-domain optical coherence tomography. , 2010, Retina.
[24] Josh Wallman,et al. The multifunctional choroid , 2010, Progress in Retinal and Eye Research.
[25] David Alonso-Caneiro,et al. Automatic segmentation of OCT retinal boundaries using recurrent neural networks and graph search. , 2018, Biomedical optics express.
[26] Deniz Erdogmus,et al. Tversky Loss Function for Image Segmentation Using 3D Fully Convolutional Deep Networks , 2017, MLMI@MICCAI.
[27] Kotagiri Ramamohanarao,et al. An automated method for choroidal thickness measurement from Enhanced Depth Imaging Optical Coherence Tomography images , 2018, Comput. Medical Imaging Graph..
[28] Andrew L. Maas. Rectifier Nonlinearities Improve Neural Network Acoustic Models , 2013 .
[29] Jerry L Prince,et al. Retinal layer segmentation of macular OCT images using boundary classification , 2013, Biomedical optics express.
[30] Joseph A. Izatt,et al. Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation , 2010, Optics express.
[31] Gema Rebolleda,et al. Use of nonmydriatic spectral-domain optical coherence tomography for diagnosing diabetic macular edema. , 2012, American journal of ophthalmology.
[32] Jian Sun,et al. Identity Mappings in Deep Residual Networks , 2016, ECCV.
[33] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[34] Yoshua Bengio,et al. ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks , 2015, ArXiv.
[35] Qingjie Liu,et al. Road Extraction by Deep Residual U-Net , 2017, IEEE Geoscience and Remote Sensing Letters.
[36] Enhua Wu,et al. Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[37] Chong Wang,et al. Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. , 2017, Biomedical optics express.
[38] David Alonso-Caneiro,et al. Effect of patch size and network architecture on a convolutional neural network approach for automatic segmentation of OCT retinal layers. , 2018, Biomedical optics express.
[39] Elise Harb,et al. Factors Associated with Macular Thickness in the COMET Myopic Cohort , 2012, Optometry and vision science : official publication of the American Academy of Optometry.
[40] G. Ripandelli,et al. Optical coherence tomography. , 1998, Seminars in ophthalmology.
[41] Delia Cabrera DeBuc,et al. A Review of Algorithms for Segmentation of Retinal Image Data Using Optical Coherence Tomography , 2011 .
[42] Nassir Navab,et al. Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks , 2018, MICCAI.
[43] K. A. Vermeer,et al. Automated segmentation by pixel classification of retinal layers in ophthalmic OCT images , 2011, Biomedical optics express.
[44] R. Spaide,et al. A pilot study of enhanced depth imaging optical coherence tomography of the choroid in normal eyes. , 2009, American journal of ophthalmology.
[45] Yoshua Bengio,et al. ReSeg: A Recurrent Neural Network-Based Model for Semantic Segmentation , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[46] Gadi Wollstein,et al. OCT for glaucoma diagnosis, screening and detection of glaucoma progression , 2013, British Journal of Ophthalmology.
[47] Ching-Yu Cheng,et al. A Simplified Method to Measure Choroidal Thickness Using Adaptive Compensation in Enhanced Depth Imaging Optical Coherence Tomography , 2014, PloS one.
[48] Masanori Hangai,et al. Three-dimensional imaging of the macular retinal nerve fiber layer in glaucoma with spectral-domain optical coherence tomography. , 2010, Investigative ophthalmology & visual science.
[49] Sébastien Ourselin,et al. Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations , 2017, DLMIA/ML-CDS@MICCAI.
[50] Soumya Jana,et al. Automated quantification of Haller’s layer in choroid using swept-source optical coherence tomography , 2018, PloS one.
[51] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[52] Yoshua Bengio,et al. Deep Sparse Rectifier Neural Networks , 2011, AISTATS.
[53] Ilya V. Kolmanovsky,et al. A neural network approach to retinal layer boundary identification from optical coherence tomography images , 2015, 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB).
[54] David Alonso-Caneiro,et al. Choroidal thickness in myopic and nonmyopic children assessed with enhanced depth imaging optical coherence tomography. , 2013, Investigative ophthalmology & visual science.
[55] Paul L. Rosin,et al. Automated choroidal segmentation of 1060 nm OCT in healthy and pathologic eyes using a statistical model , 2011, Biomedical optics express.
[56] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[57] Navdeep Jaitly,et al. Towards End-To-End Speech Recognition with Recurrent Neural Networks , 2014, ICML.
[58] Bolun Cai,et al. FReLU: Flexible Rectified Linear Units for Improving Convolutional Neural Networks , 2018, 2018 24th International Conference on Pattern Recognition (ICPR).
[59] Yupeng Xu,et al. Dual-stage deep learning framework for pigment epithelium detachment segmentation in polypoidal choroidal vasculopathy. , 2017, Biomedical optics express.
[60] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[61] Yilong Yin,et al. Choroid segmentation from Optical Coherence Tomography with graph-edge weights learned from deep convolutional neural networks , 2017, Neurocomputing.
[62] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[63] Thomas Brox,et al. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation , 2016, MICCAI.
[64] Geoffrey E. Hinton,et al. Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[65] Nassir Navab,et al. ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network , 2017, Biomedical optics express.
[66] Xiaodong Wu,et al. Simultaneous Multiple Surface Segmentation Using Deep Learning , 2017, DLMIA/ML-CDS@MICCAI.
[67] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[68] Sina Farsiu,et al. Validation of Macular Choroidal Thickness Measurements from Automated SD-OCT Image Segmentation , 2016, Optometry and vision science : official publication of the American Academy of Optometry.
[69] David Alonso-Caneiro,et al. MACULAR RETINAL LAYER THICKNESS IN CHILDHOOD , 2015, Retina.
[70] David Alonso-Caneiro,et al. Automatic Retinal and Choroidal Boundary Segmentation in OCT Images Using Patch-Based Supervised Machine Learning Methods , 2018, ACCV Workshops.
[71] M. Sonka,et al. Automated segmentation of the choroid from clinical SD-OCT. , 2012, Investigative ophthalmology & visual science.
[72] Yoshua Bengio,et al. How transferable are features in deep neural networks? , 2014, NIPS.
[73] Zeyun Yu,et al. State-of-the-Art in Retinal Optical Coherence Tomography Image Analysis , 2014, Quantitative imaging in medicine and surgery.
[74] Sina Farsiu,et al. Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology. , 2014, Biomedical optics express.
[75] Seyed-Ahmad Ahmadi,et al. V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).
[76] Milan Sonka,et al. Choroidal thickness maps from spectral domain and swept source optical coherence tomography: algorithmic versus ground truth annotation , 2016, British Journal of Ophthalmology.
[77] Roberto Cipolla,et al. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[78] José García Rodríguez,et al. A Review on Deep Learning Techniques Applied to Semantic Segmentation , 2017, ArXiv.
[79] Tin Aung Tun,et al. Automatic segmentation of the choroid in enhanced depth imaging optical coherence tomography images , 2013, Biomedical optics express.
[80] Christopher Joseph Pal,et al. The Importance of Skip Connections in Biomedical Image Segmentation , 2016, LABELS/DLMIA@MICCAI.
[81] Sina Farsiu,et al. Deep longitudinal transfer learning-based automatic segmentation of photoreceptor ellipsoid zone defects on optical coherence tomography images of macular telangiectasia type 2 , 2018, Biomedical optics express.
[82] Seunghoon Hong,et al. Learning Deconvolution Network for Semantic Segmentation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).