Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks
暂无分享,去创建一个
Sina Farsiu | Leyuan Fang | Alfredo Dubra | Robert F. Cooper | David Cunefare | Joseph Carroll | A. Dubra | Sina Farsiu | Leyuan Fang | J. Carroll | R. F. Cooper | David Cunefare
[1] Sina Farsiu,et al. Automatic detection of cone photoreceptors in split detector adaptive optics scanning light ophthalmoscope images , 2016, Biomedical optics express.
[2] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[3] Bing Wu,et al. Automated analysis of differential interference contrast microscopy images of the foveal cone mosaic. , 2008, Journal of the Optical Society of America. A, Optics, image science, and vision.
[4] A. Tsujikawa,et al. High-resolution imaging of resolved central serous chorioretinopathy using adaptive optics scanning laser ophthalmoscopy. , 2010, Ophthalmology.
[5] David Williams,et al. Optical fiber properties of individual human cones. , 2002, Journal of vision.
[6] D. Altman,et al. STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT , 1986, The Lancet.
[7] Christopher S. Langlo,et al. Automatic detection of modal spacing (Yellott's ring) in adaptive optics scanning light ophthalmoscope images , 2013, Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians.
[8] Stephen A. Burns,et al. Cone Photoreceptor Irregularity on Adaptive Optics Scanning Laser Ophthalmoscopy Correlates With Severity of Diabetic Retinopathy and Macular Edema , 2016, Investigative ophthalmology & visual science.
[9] Joseph A. Izatt,et al. Automatic cone photoreceptor segmentation using graph theory and dynamic programming , 2013, Biomedical optics express.
[10] Julia S. Kroisamer,et al. Temporal changes of human cone photoreceptors observed in vivo with SLO/OCT , 2010, Biomedical optics express.
[11] T. Hebert,et al. Adaptive optics scanning laser ophthalmoscopy. , 2002, Optics express.
[12] ee,et al. Multi-modal automatic montaging of adaptive optics retinal images , 2016 .
[13] A. Dubra,et al. Reflective afocal broadband adaptive optics scanning ophthalmoscope , 2011, Biomedical optics express.
[14] P. Mahadevan,et al. An overview , 2007, Journal of Biosciences.
[15] Daniel X Hammer,et al. Adaptive optics scanning laser ophthalmoscope with integrated wide-field retinal imaging and tracking. , 2010, Journal of the Optical Society of America. A, Optics, image science, and vision.
[16] C. Dainty,et al. Adaptive optics enhanced simultaneous en-face optical coherence tomography and scanning laser ophthalmoscopy. , 2006, Optics express.
[17] Christopher S. Langlo,et al. In vivo imaging of human cone photoreceptor inner segments. , 2014, Investigative ophthalmology & visual science.
[18] Subhashini Venugopalan,et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.
[19] John S Werner,et al. In vivo imaging of the photoreceptor mosaic in retinal dystrophies and correlations with visual function. , 2006, Investigative ophthalmology & visual science.
[20] Debjani Chakraborty,et al. Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration. , 2017, Biomedical optics express.
[21] Christopher S. Langlo,et al. Repeatability of In Vivo Parafoveal Cone Density and Spacing Measurements , 2012, Optometry and vision science : official publication of the American Academy of Optometry.
[22] Pierre Soille,et al. Morphological Image Analysis: Principles and Applications , 2003 .
[23] Ronald M. Summers,et al. Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique , 2016 .
[24] M. Lombardo,et al. Technical Factors Influencing Cone Packing Density Estimates in Adaptive Optics Flood Illuminated Retinal Images , 2014, PloS one.
[25] Fred K Chen,et al. Semi-automated identification of cones in the human retina using circle Hough transform. , 2015, Biomedical optics express.
[26] Jennifer J. Hunter,et al. Imaging individual neurons in the retinal ganglion cell layer of the living eye , 2017, Proceedings of the National Academy of Sciences.
[27] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[28] Steven M. Jones,et al. Adaptive-optics optical coherence tomography for high-resolution and high-speed 3 D retinal in vivo imaging , 2005 .
[29] Michael Pircher,et al. Adaptive optics SLO/OCT for 3D imaging of human photoreceptors in vivo. , 2014, Biomedical optics express.
[30] Krzysztof Krawiec,et al. Segmenting Retinal Blood Vessels With Deep Neural Networks , 2016, IEEE Transactions on Medical Imaging.
[31] Yann LeCun,et al. What is the best multi-stage architecture for object recognition? , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[32] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[33] T. Mihashi,et al. In Vivo Measurements of Cone Photoreceptor Spacing in Myopic Eyes from Images Obtained by an Adaptive Optics Fundus Camera , 2007, Japanese Journal of Ophthalmology.
[34] Takashi Fujikado,et al. Detection of photoreceptor disruption by adaptive optics fundus imaging and Fourier-domain optical coherence tomography in eyes with occult macular dystrophy , 2011, Clinical ophthalmology.
[35] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[36] Andrea Vedaldi,et al. MatConvNet: Convolutional Neural Networks for MATLAB , 2014, ACM Multimedia.
[37] Philip J. Morrow,et al. Automated Identification of Photoreceptor Cones Using Multi-scale Modelling and Normalized Cross-Correlation , 2011, ICIAP.
[38] L. R. Dice. Measures of the Amount of Ecologic Association Between Species , 1945 .
[39] A. Roorda,et al. Observation of cone and rod photoreceptors in normal subjects and patients using a new generation adaptive optics scanning laser ophthalmoscope , 2011, Biomedical optics express.
[40] A. Hendrickson,et al. Human photoreceptor topography , 1990, The Journal of comparative neurology.
[41] Jürgen Schmidhuber,et al. Deep learning in neural networks: An overview , 2014, Neural Networks.
[42] W. Drexler,et al. Adaptive optics optical coherence tomography at 120,000 depth scans/s for non-invasive cellular phenotyping of the living human retina. , 2009, Optics express.
[43] Michael I. Jordan,et al. Machine learning: Trends, perspectives, and prospects , 2015, Science.
[44] Toco Y P Chui,et al. The use of forward scatter to improve retinal vascular imaging with an adaptive optics scanning laser ophthalmoscope , 2012, Biomedical optics express.
[45] Tianfu Wang,et al. A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images , 2016, IEEE Transactions on Medical Imaging.
[46] Robert J Zawadzki,et al. Fourier-Domain Optical Coherence Tomography and Adaptive Optics Reveal Nerve Fiber Layer Loss and Photoreceptor Changes in a Patient With Optic Nerve Drusen , 2008, Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society.
[47] Bram van Ginneken,et al. Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images , 2016, IEEE Transactions on Medical Imaging.
[48] Rashid Ansari,et al. Frequency-based local content adaptive filtering algorithm for automated photoreceptor cell density quantification , 2012, 2012 19th IEEE International Conference on Image Processing.
[49] Austin Roorda,et al. Automated identification of cone photoreceptors in adaptive optics retinal images. , 2007, Journal of the Optical Society of America. A, Optics, image science, and vision.
[50] David Williams,et al. The arrangement of the three cone classes in the living human eye , 1999, Nature.
[51] Christopher S. Langlo,et al. Reliability and Repeatability of Cone Density Measurements in Patients with Congenital Achromatopsia. , 2016, Advances in experimental medicine and biology.
[52] Nicholas Devaney,et al. Performance Analysis of Cone Detection Algorithms , 2015, Journal of the Optical Society of America. A, Optics, image science, and vision.
[53] Toco Y P Chui,et al. Adaptive-optics imaging of human cone photoreceptor distribution. , 2008, Journal of the Optical Society of America. A, Optics, image science, and vision.
[54] John S Werner,et al. Photoreceptor counting and montaging of en-face retinal images from an adaptive optics fundus camera. , 2007, Journal of the Optical Society of America. A, Optics, image science, and vision.
[55] Shutao Li,et al. Segmentation Based Sparse Reconstruction of Optical Coherence Tomography Images , 2017, IEEE Transactions on Medical Imaging.
[56] Stephen Lin,et al. DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field , 2016, MICCAI.
[57] Stephen A. Burns,et al. The organization of the cone photoreceptor mosaic measured in the living human retina , 2017, Vision Research.
[58] Ravi S. Jonnal,et al. Imaging cone photoreceptors in three dimensions and in time using ultrahigh resolution optical coherence tomography with adaptive optics , 2011, Biomedical optics express.
[59] Omer P. Kocaoglu,et al. Phase-sensitive imaging of the outer retina using optical coherence tomography and adaptive optics , 2011, Biomedical optics express.
[60] A. Dubra,et al. In vivo dark-field imaging of the retinal pigment epithelium cell mosaic. , 2013, Biomedical optics express.
[61] David Williams,et al. Noninvasive imaging of the human rod photoreceptor mosaic using a confocal adaptive optics scanning ophthalmoscope , 2011, Biomedical optics express.
[62] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[63] A. Roorda,et al. Adaptive optics ophthalmoscopy. , 2015, Annual review of vision science.
[64] A. Dubra,et al. Subclinical photoreceptor disruption in response to severe head trauma. , 2012, Archives of ophthalmology.
[65] 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.
[66] S. Tarima,et al. Evaluating Descriptive Metrics of the Human Cone Mosaic , 2016, Investigative ophthalmology & visual science.
[67] Christopher S. Langlo,et al. Assessing Photoreceptor Structure in Retinitis Pigmentosa and Usher Syndrome , 2016, Investigative ophthalmology & visual science.
[68] T. Sørensen,et al. A method of establishing group of equal amplitude in plant sociobiology based on similarity of species content and its application to analyses of the vegetation on Danish commons , 1948 .
[69] A. Swaroop,et al. High-resolution imaging with adaptive optics in patients with inherited retinal degeneration. , 2007, Investigative ophthalmology & visual science.