Multilayered Deep Structure Tensor Delaunay Triangulation and Morphing Based Automated Diagnosis and 3D Presentation of Human Macula

Maculopathy is the group of diseases that affects central vision of a person and they are often associated with diabetes. Many researchers reported automated diagnosis of maculopathy from optical coherence tomography (OCT) images. However, to the best of our knowledge there is no literature that presents a complete 3D suite for the extraction as well as diagnosis of macula. Therefore, this paper presents a multilayered convolutional neural networks (CNN) structure tensor Delaunay triangulation and morphing based fully autonomous system that extracts up to nine retinal and choroidal layers along with the macular fluids. Furthermore, the proposed system utilizes the extracted retinal information for the automated diagnosis of maculopathy as well as for the robust reconstruction of 3D macula of retina. The proposed system has been validated on 41,921 retinal OCT scans acquired from different OCT machines and it significantly outperformed existing state of the art solutions by achieving the mean accuracy of 95.27% for extracting retinal and choroidal layers, mean dice coefficient of 0.90 for extracting fluid pathology and the overall accuracy of 96.07% for maculopathy diagnosis. To the best of our knowledge, the proposed framework is first of its kind that provides a fully automated and complete 3D integrated solution for the extraction of candidate macula along with its fully automated diagnosis against different macular syndromes.

[1]  Taimur Hassan,et al.  Automated diagnosis of macular edema and central serous retinopathy through robust reconstruction of 3D retinal surfaces , 2016, Comput. Methods Programs Biomed..

[2]  Sina Farsiu,et al.  Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema. , 2015, Biomedical optics express.

[3]  Keshab K. Parhi,et al.  Fully Automated Segmentation of Fluid/Cyst Regions in Optical Coherence Tomography Images With Diabetic Macular Edema Using Neutrosophic Sets and Graph Algorithms , 2018, IEEE Transactions on Biomedical Engineering.

[4]  Chong Wang,et al.  Automatic segmentation of nine layer boundaries in OCT images using convolutional neural networks and graph search , 2017 .

[5]  Richard S. Snell,et al.  Clinical Anatomy of the Eye , 1990 .

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

[7]  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.

[8]  Qi Yang,et al.  Automated layer segmentation of macular OCT images using dual-scale gradient information. , 2010, Optics express.

[9]  Peter K Kaiser,et al.  Optical coherence tomography imaging of macular oedema , 2014, British Journal of Ophthalmology.

[10]  Taimur Hassan,et al.  Structure tensor based automated detection of macular edema and central serous retinopathy using optical coherence tomography images. , 2016, Journal of the Optical Society of America. A, Optics, image science, and vision.

[11]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[12]  E Reichel,et al.  Topography of diabetic macular edema with optical coherence tomography. , 1998, Ophthalmology.

[13]  José Luis Villaseñor,et al.  Estimating the effect of the similarity coeffi cient and the cluster algorithm on biogeographic classifi cations , 2003 .

[14]  Yoshua Bengio,et al.  Practical Recommendations for Gradient-Based Training of Deep Architectures , 2012, Neural Networks: Tricks of the Trade.

[15]  S. Rebay.  Efficient Unstructured Mesh Generation by Means of Delaunay Triangulation and Bowyer-Watson Algorithm , 1993 .

[16]  Xinjian Chen,et al.  Automated segmentation of intraretinal cystoid macular edema for retinal 3D OCT images with macular hole , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[17]  Joseph A. Izatt,et al.  Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation , 2010, Optics express.

[18]  Gianni Virgili,et al.  Optical coherence tomography (OCT) for detection of macular oedema in patients with diabetic retinopathy. , 2011, The Cochrane database of systematic reviews.

[19]  Marilita M Moschos,et al.  Cystoid macular edema , 2008, Clinical ophthalmology.

[20]  Carmen A Puliafito,et al.  Automated detection of retinal layer structures on optical coherence tomography images. , 2005, Optics express.

[21]  Sina Farsiu,et al.  Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images. , 2014, Biomedical optics express.

[22]  Richard S. Snell,et al.  [Paranasal sinuses]. , 1984, Archives of oto-rhino-laryngology. Supplement = Archiv fur Ohren-, Nasen- und Kehlkopfheilkunde. Supplement.

[23]  Adeel M. Syed,et al.  Automated segmentation of subretinal layers for the detection of macular edema. , 2016, Applied optics.

[24]  M. Ávila,et al.  Detection of diabetic foveal edema with biomicroscopy, fluorescein angiography and optical coherence tomography. , 2008, Arquivos brasileiros de oftalmologia.

[25]  J. Duker,et al.  Intravitreal triamcinolone for refractory diabetic macular edema. , 2002, Ophthalmology.

[26]  A Shrestha,et al.  Optical coherence tomographic assessment of macular thickness and morphological patterns in diabetic macular edema: prognosis after modified grid photocoagulation. , 2012, Nepalese journal of ophthalmology : a biannual peer-reviewed academic journal of the Nepal Ophthalmic Society : NEPJOPH.

[27]  Xian Zhang,et al.  Development of a Semi-Automatic Segmentation Method for Retinal OCT Images Tested in Patients with Diabetic Macular Edema , 2013, PloS one.

[28]  Yasser M Helmy,et al.  Optical coherence tomography classification of diabetic cystoid macular edema , 2013, Clinical ophthalmology.

[29]  Gulistan Raja,et al.  Fully automated assessment of Macular Edema using Optical Coherence Tomography (OCT) images , 2016, 2016 International Conference on Intelligent Systems Engineering (ICISE).

[30]  Desislava Koleva-Georgieva,et al.  Optical Coherence Tomography Findings in Diabetic Macular Edema , 2012 .

[31]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  G. Ferreira-Dias,et al.  Cytokines and Angiogenesis in the Corpus Luteum , 2013, Mediators of inflammation.

[33]  Amy L. Oldenburg,et al.  Automated Segmentation of Intraretinal Cystoid Fluid in Optical Coherence Tomography , 2012, IEEE Transactions on Biomedical Engineering.

[34]  Bartosz L. Sikorski,et al.  The Diagnostic Function of OCT in Diabetic Maculopathy , 2013, Mediators of inflammation.

[35]  Adisom Leelasantitham,et al.  Automated classification between age-related macular degeneration and Diabetic macular edema in OCT image using image segmentation , 2014, The 7th 2014 Biomedical Engineering International Conference.

[36]  X Liu,et al.  Retina layer segmentation using kernel graph cuts and continuous max-flow. , 2015, Optics express.

[37]  Michael D. Ober,et al.  Diagnosis of Age Related Macular Degeneration. , 2006 .

[38]  Eric L Yuan,et al.  Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography. , 2014, Ophthalmology.

[39]  Shyam Vasudeva Rao,et al.  Segmentation and analysis of retinal layers (ILM & RPE) in Optical Coherence Tomography images with Edema , 2014, 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES).