CDED-Net: Joint Segmentation of Optic Disc and Optic Cup for Glaucoma Screening

Glaucoma is an eye disease that can cause loss of vision by damaging the optic nerve. It is the world’s second leading cause of blindness after cataracts. Early diagnosis of glaucoma is a key to prevent permanent blindness as it has no noticeable symptoms in its early stages. Color fundus photography is used for examining the optic disc (OD) which is an important step in the diagnoses of glaucoma. This is done by estimating the cup-to-disc ratio (CDR). In this paper, we proposed a Cup Disc Encoder Decoder Network (CDED-Net) for the joint segmentation of optic disc (OD) and optic cup (OC). We have eradicated the pre-processing and post-processing steps to reduce the computational cost of the overall system. Segmentation of (OD) and OC is modeled as a semantic pixel-wise labeling problem. The model was trained on the DRISHTI-GS, RIM-ONE and REFUGE datasets. Experiments show that our CDED-Net system achieves state-of-the-art OD and OC segmentation results on these datasets.

[1]  Tien Yin Wong,et al.  Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening , 2013, IEEE Transactions on Medical Imaging.

[2]  Mamta Juneja,et al.  Survey on segmentation and classification approaches of optic cup and optic disc for diagnosis of glaucoma , 2018, Biomed. Signal Process. Control..

[3]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Xiaoxiao Li,et al.  REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs , 2019, Medical Image Anal..

[5]  Heye Zhang,et al.  Joint optic disc and cup segmentation using semi-supervised conditional GANs , 2019, Comput. Biol. Medicine.

[6]  Jayanthi Sivaswamy,et al.  Glaucoma classification with a fusion of segmentation and image-based features , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[7]  Xiaochun Cao,et al.  Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation , 2018, IEEE Transactions on Medical Imaging.

[8]  Sang Jun Park,et al.  Towards Accurate Segmentation of Retinal Vessels and the Optic Disc in Fundoscopic Images with Generative Adversarial Networks , 2018, Journal of Digital Imaging.

[9]  Ashish Issac,et al.  An automated and robust image processing algorithm for glaucoma diagnosis from fundus images using novel blood vessel tracking and bend point detection , 2018, Int. J. Medical Informatics.

[10]  W. Ruengkitpinyo,et al.  Glaucoma screening using rim width based on ISNT rule , 2015, 2015 6th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES).

[11]  U. Rajendra Acharya,et al.  Computer-aided diagnosis of glaucoma using fundus images: A review , 2018, Comput. Methods Programs Biomed..

[12]  Jayanthi Sivaswamy,et al.  RACE-Net: A Recurrent Neural Network for Biomedical Image Segmentation , 2019, IEEE Journal of Biomedical and Health Informatics.

[13]  Tien Yin Wong,et al.  Automatic optic disc segmentation with peripapillary atrophy elimination , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[14]  Hamdani Hamdani,et al.  Automatic Glaucoma Detection Method Applying a Statistical Approach to Fundus Images , 2018, Healthcare informatics research.

[15]  Shenghua Gao,et al.  CE-Net: Context Encoder Network for 2D Medical Image Segmentation , 2019, IEEE Transactions on Medical Imaging.

[16]  Mohammad Sohel Rahman,et al.  MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation , 2019, Neural Networks.

[17]  Jayanthi Sivaswamy,et al.  A Comprehensive Retinal Image Dataset for the Assessment of Glaucoma from the Optic Nerve Head Analysis , 2015 .

[18]  Ning Tan,et al.  Optic Disc and Cup Segmentation Based on Deep Convolutional Generative Adversarial Networks , 2019, IEEE Access.

[19]  Matthew B. Blaschko,et al.  Convolutional neural network transfer for automated glaucoma identification , 2017, Symposium on Medical Information Processing and Analysis.

[20]  Yugen Yi,et al.  Optic Disc and Cup Segmentation in Retinal Images for Glaucoma Diagnosis by Locally Statistical Active Contour Model with Structure Prior , 2019, Comput. Math. Methods Medicine.

[21]  Ashish Issac,et al.  An adaptive threshold based algorithm for optic disc and cup segmentation in fundus images , 2015, 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN).

[22]  Safak Bayir,et al.  Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques , 2016, Comput. Math. Methods Medicine.

[23]  Chandra Sekhar Seelamantula,et al.  Active discs for automated optic disc segmentation , 2015, 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[24]  Aini Hussain,et al.  An automated glaucoma screening system using cup-to-disc ratio via Simple Linear Iterative Clustering superpixel approach , 2019, Biomed. Signal Process. Control..

[25]  Fulufhelo V. Nelwamondo,et al.  Segmentation of Optic Cup and Disc for Diagnosis of Glaucoma on Retinal Fundus Images , 2019, 2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA).

[26]  H. Quigley,et al.  The number of people with glaucoma worldwide in 2010 and 2020 , 2006, British Journal of Ophthalmology.

[27]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[28]  Anjan Gudigar,et al.  Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images , 2018, Inf. Sci..

[29]  Nima Tajbakhsh,et al.  UNet++: A Nested U-Net Architecture for Medical Image Segmentation , 2018, DLMIA/ML-CDS@MICCAI.

[30]  Malay Kishore Dutta,et al.  A novel approach to detect glaucoma in retinal fundus images using cup-disk and rim-disk ratio , 2015, 2015 4th International Work Conference on Bioinspired Intelligence (IWOBI).

[31]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Xiaochun Cao,et al.  Disc-Aware Ensemble Network for Glaucoma Screening From Fundus Image , 2018, IEEE Transactions on Medical Imaging.

[33]  Jayanthi Sivaswamy,et al.  Optic disk and cup boundary detection using regional information , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[34]  A. Sevastopolsky,et al.  Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network , 2017, Pattern Recognition and Image Analysis.

[35]  Jayanthi Sivaswamy,et al.  Optic Disk and Cup Segmentation From Monocular Color Retinal Images for Glaucoma Assessment , 2011, IEEE Transactions on Medical Imaging.

[36]  Kaamran Raahemifar,et al.  Optic Disc and Optic Cup Segmentation Methodologies for Glaucoma Image Detection: A Survey , 2015, Journal of ophthalmology.

[37]  Rafael Arnay,et al.  Ant Colony Optimization-based method for optic cup segmentation in retinal images , 2017, Appl. Soft Comput..

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

[39]  Joachim M. Buhmann,et al.  Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation , 2017, Comput. Medical Imaging Graph..

[40]  Sangsoo Kim,et al.  A deep learning model for the detection of both advanced and early glaucoma using fundus photography , 2018, PloS one.

[41]  M. Usman Akram,et al.  Improved automated detection of glaucoma from fundus image using hybrid structural and textural features , 2017, IET Image Process..

[42]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[43]  Keerthi Ram,et al.  Fully Convolutional Networks for Monocular Retinal Depth Estimation and Optic Disc-Cup Segmentation , 2019, IEEE Journal of Biomedical and Health Informatics.

[44]  Agus Harjoko,et al.  Automated Detection of Retinal Nerve Fiber Layer by Texture-Based Analysis for Glaucoma Evaluation , 2018, Healthcare informatics research.

[45]  Ana Cristina Murillo,et al.  EV-SegNet: Semantic Segmentation for Event-Based Cameras , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[46]  Muhammad Usman Akram,et al.  Clinical and technical perspective of glaucoma detection using OCT and fundus images: A review , 2017, 2017 1st International Conference on Next Generation Computing Applications (NextComp).

[47]  Valery Naranjo,et al.  CNNs for automatic glaucoma assessment using fundus images: an extensive validation , 2019, BioMedical Engineering OnLine.

[48]  Yogesan Kanagasingam,et al.  Robust optic disc and cup segmentation with deep learning for glaucoma detection , 2019, Comput. Medical Imaging Graph..

[49]  Andrea Gallo,et al.  Glaucoma: recent advances in the involvement of autoimmunity , 2017, Immunologic research.

[50]  Baihua Li,et al.  A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis , 2017, Journal of Medical Systems.

[51]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[52]  Amit Kale,et al.  Automatic Optic Disk and Cup Segmentation of Fundus Images Using Deep Learning , 2018, 2018 25th IEEE International Conference on Image Processing (ICIP).

[53]  Manuel Emilio Gegúndez-Arias,et al.  Detecting the Optic Disc Boundary in Digital Fundus Images Using Morphological, Edge Detection, and Feature Extraction Techniques , 2010, IEEE Transactions on Medical Imaging.

[54]  Han-Xiong Li,et al.  Mixed Maximum Loss Design for Optic Disc and Optic Cup Segmentation with Deep Learning from Imbalanced Samples , 2019, Sensors.

[55]  Chi-Wing Fu,et al.  Patch-Based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation , 2019, IEEE Transactions on Medical Imaging.

[56]  Fabio A. González,et al.  Glaucoma Diagnosis from Eye Fundus Images Based on Deep Morphometric Feature Estimation , 2018, COMPAY/OMIA@MICCAI.

[57]  Dwarikanath Mahapatra,et al.  Segmentation of optic disc and optic cup in retinal fundus images using shape regression , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[58]  Francisco Fumero,et al.  RIM-ONE: An open retinal image database for optic nerve evaluation , 2011, 2011 24th International Symposium on Computer-Based Medical Systems (CBMS).

[59]  Majid A. Al-Taee,et al.  Dense Fully Convolutional Segmentation of the Optic Disc and Cup in Colour Fundus for Glaucoma Diagnosis , 2018, Symmetry.

[60]  H. Quigley.  Number of people with glaucoma worldwide. , 1996, The British journal of ophthalmology.