Automatic segmentation of retinal layers in OCT images with intermediate age-related macular degeneration using U-Net and DexiNed

Age-related macular degeneration (AMD) is an eye disease that can cause visual impairment and affects the elderly over 50 years of age. AMD is characterized by the presence of drusen, which causes changes in the physiological structure of the retinal pigment epithelium (RPE) and the boundaries of the Bruch’s membrane layer (BM). Optical coherence tomography is one of the main exams for the detection and monitoring of AMD, which seeks changes through the evaluation of successive sectional cuts in the search for morphological changes caused by drusen. The use of CAD (Computer-Aided Detection) systems has contributed to increasing the chances of correct detection, assisting specialists in diagnosing and monitoring disease. Thus, the objective of this work is to present a method for the segmentation of the inner limiting membrane (ILM), retinal pigment epithelium, and Bruch’s membrane in OCT images of healthy and Intermediate AMD patients. The method uses two deep neural networks, U-Net and DexiNed to perform the segmentation. The results were promising, reaching an average absolute error of 0.49 pixel for ILM, 0.57 for RPE, and 0.66 for BM.

[1]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[2]  Gábor Márk Somfai,et al.  Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region , 2015, PloS one.

[3]  Jayanthi Sivaswamy,et al.  End-to-End Learning of a Conditional Random Field for Intra-retinal Layer Segmentation in Optical Coherence Tomography , 2017, MIUA.

[4]  Saining Xie,et al.  Holistically-Nested Edge Detection , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[5]  Yi Wang,et al.  Fast Retinal Segmentation Based on the Wave Algorithm , 2020, IEEE Access.

[6]  Sina Farsiu,et al.  Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images. , 2012, Investigative ophthalmology & visual science.

[7]  R. Klein,et al.  Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis. , 2014, The Lancet. Global health.

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

[9]  Hao Wei,et al.  Automated retinal layer segmentation in OCT images of age-related macular degeneration , 2019, IET Image Process..

[10]  François Chollet,et al.  Keras: The Python Deep Learning library , 2018 .

[11]  Richard F Spaide,et al.  COMPARISON OF FLUORESCEIN ANGIOGRAPHY AND OPTICAL COHERENCE TOMOGRAPHY FOR PATIENTS WITH CHOROIDAL NEOVASCULARIZATION AFTER PHOTODYNAMIC THERAPY , 2005, Retina.

[12]  A. Sappa,et al.  Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection , 2019, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

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

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

[15]  Sina Farsiu,et al.  Fast detection and segmentation of drusen in retinal optical coherence tomography images , 2008, SPIE BiOS.

[16]  Jayanthi Sivaswamy,et al.  A Deep Learning Framework for Segmentation of Retinal Layers from OCT Images , 2017, 2017 4th IAPR Asian Conference on Pattern Recognition (ACPR).

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

[18]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[19]  David S Friedman,et al.  Age-related eye diseases and visual impairment among U.S. adults. , 2013, American journal of preventive medicine.

[20]  David Alonso-Caneiro,et al.  Automatic segmentation of OCT retinal boundaries using recurrent neural networks and graph search. , 2018, Biomedical optics express.

[21]  Frederick Lippman,et al.  Blindness and vision impairment , 2008 .

[22]  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.