Development and validation of a deep learning algorithm for distinguishing the nonperfusion area from signal reduction artifacts on OCT angiography.

The capillary nonperfusion area (NPA) is a key quantifiable biomarker in the evaluation of diabetic retinopathy (DR) using optical coherence tomography angiography (OCTA). However, signal reduction artifacts caused by vitreous floaters, pupil vignetting, or defocus present significant obstacles to accurate quantification. We have developed a convolutional neural network, MEDnet-V2, to distinguish NPA from signal reduction artifacts in 6×6 mm2 OCTA. The network achieves strong specificity and sensitivity for NPA detection across a wide range of DR severity and scan quality.

[1]  David Huang,et al.  MEDnet, a neural network for automated detection of avascular area in OCT angiography. , 2018, Biomedical optics express.

[2]  Qienyuan Zhou,et al.  RETINAL VASCULAR PERFUSION DENSITY MAPPING USING OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY IN NORMALS AND DIABETIC RETINOPATHY PATIENTS , 2015, Retina.

[3]  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.

[4]  Daniel S. Kermany,et al.  Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning , 2018, Cell.

[5]  Nassir Navab,et al.  ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network , 2017, Biomedical optics express.

[6]  Joachim Hornegger,et al.  AN AUTOMATIC, INTERCAPILLARY AREA-BASED ALGORITHM FOR QUANTIFYING DIABETES-RELATED CAPILLARY DROPOUT USING OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY , 2016, Retina.

[7]  Thomas Theelen,et al.  Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks. , 2017, Biomedical optics express.

[8]  David J. Wilson,et al.  OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY FEATURES OF DIABETIC RETINOPATHY , 2015, Retina.

[9]  David J. Wilson,et al.  Automated Quantification of Nonperfusion Areas in 3 Vascular Plexuses With Optical Coherence Tomography Angiography in Eyes of Patients With Diabetes , 2018, JAMA ophthalmology.

[10]  David Huang,et al.  Visualization of 3 Distinct Retinal Plexuses by Projection-Resolved Optical Coherence Tomography Angiography in Diabetic Retinopathy. , 2016, JAMA ophthalmology.

[11]  James G. Fujimoto,et al.  Optical coherence tomography angiography , 2017, Progress in Retinal and Eye Research.

[12]  Thomas S. Hwang,et al.  Automated Quantification of Nonperfusion in Three Retinal Plexuses Using Projection-Resolved Optical Coherence Tomography Angiography in Diabetic Retinopathy , 2016, Investigative ophthalmology & visual science.

[13]  Thomas Theelen,et al.  Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography. , 2018, Biomedical optics express.

[14]  K. Prodanova,et al.  Modeling data for tilted implants in grafted with bio-oss maxillary sinuses using logistic regression , 2014 .

[15]  David Huang,et al.  Reflectance-based projection-resolved optical coherence tomography angiography [Invited]. , 2017, Biomedical optics express.

[16]  David Huang,et al.  Projection-resolved optical coherence tomographic angiography. , 2016, Biomedical optics express.

[17]  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.

[18]  David Huang,et al.  Automated detection of shadow artifacts in optical coherence tomography angiography. , 2019, Biomedical optics express.

[19]  Martin F. Kraus,et al.  Split-spectrum amplitude-decorrelation angiography with optical coherence tomography , 2012, Optics express.

[20]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[21]  Aaron Y. Lee,et al.  Artificial intelligence and deep learning in ophthalmology , 2018, British Journal of Ophthalmology.

[22]  K Muraoka,et al.  Distribution of capillary nonperfusion in early-stage diabetic retinopathy. , 1984, Ophthalmology.

[23]  Lei Liu,et al.  Quantifying Microvascular Abnormalities With Increasing Severity of Diabetic Retinopathy Using Optical Coherence Tomography Angiography , 2017, Investigative ophthalmology & visual science.

[24]  Yue Wu,et al.  Deep-Learning Based, Automated Segmentation of Macular Edema in Optical Coherence Tomography , 2017, bioRxiv.

[25]  Jie Wang,et al.  Automated segmentation of retinal layer boundaries and capillary plexuses in wide-field optical coherence tomographic angiography , 2018, Biomedical optics express.

[26]  M. Treder,et al.  Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning , 2018, Graefe's Archive for Clinical and Experimental Ophthalmology.