TY - JOUR
T1 - A computer-aided diagnostic system for detecting diabetic retinopathy in optical coherence tomography images
AU - Eltanboly, Ahmed
AU - Ismail, Marwa
AU - Shalaby, Ahmed
AU - Switala, Andy
AU - El-Baz, Ayman
AU - Schaal, Shlomit
AU - Gimel’farb, Georgy
AU - El-Azab, Magdi
N1 - Publisher Copyright:
© 2016 American Association of Physicists in Medicine.
PY - 2017/3
Y1 - 2017/3
N2 - Purpose: Detection (diagnosis) of diabetic retinopathy (DR) in optical coherence tomography (OCT) images for patients with type 2 diabetes, but almost clinically normal retina appearances. Methods: The proposed computer-aided diagnostic (CAD) system detects the DR in three steps: (a) localizing and segmenting 12 distinct retinal layers on the OCT image; (b) deriving features of the segmented layers, and (c) learning most discriminative features and classifying each subject as normal or diabetic. To localise and segment the retinal layers, signals (intensities) of the OCT image are described with a joint Markov-Gibbs random field (MGRF) model of intensities and shape descriptors. Each segmented layer is characterized with cumulative probability distribution functions (CDF) of its locally extracted features, such as reflectivity, curvature, and thickness. A multistage deep fusion classification network (DFCN) with a stack of non-negativity-constrained autoencoders (NCAE) is trained to select the most discriminative retinal layers’ features and use their CDFs for detecting the DR. A training atlas was built using the OCT scans for 12 normal subjects and their maps of layers hand-drawn by retina experts. Results: Preliminary experiments on 52 clinical OCT scans (26 normal and 26 with early-stage DR, balanced between 40–79 yr old males and females; 40 training and 12 test subjects) gave the DR detection accuracy, sensitivity, and specificity of 92%; 83%, and 100%, respectively. The 100% accuracy, sensitivity, and specificity have been obtained in the leave-one-out cross-validation test for all the 52 subjects. Conclusion: Both the quantitative and visual assessments confirmed the high accuracy of the proposed computer-assisted diagnostic system for early DR detection using the OCT retinal images.
AB - Purpose: Detection (diagnosis) of diabetic retinopathy (DR) in optical coherence tomography (OCT) images for patients with type 2 diabetes, but almost clinically normal retina appearances. Methods: The proposed computer-aided diagnostic (CAD) system detects the DR in three steps: (a) localizing and segmenting 12 distinct retinal layers on the OCT image; (b) deriving features of the segmented layers, and (c) learning most discriminative features and classifying each subject as normal or diabetic. To localise and segment the retinal layers, signals (intensities) of the OCT image are described with a joint Markov-Gibbs random field (MGRF) model of intensities and shape descriptors. Each segmented layer is characterized with cumulative probability distribution functions (CDF) of its locally extracted features, such as reflectivity, curvature, and thickness. A multistage deep fusion classification network (DFCN) with a stack of non-negativity-constrained autoencoders (NCAE) is trained to select the most discriminative retinal layers’ features and use their CDFs for detecting the DR. A training atlas was built using the OCT scans for 12 normal subjects and their maps of layers hand-drawn by retina experts. Results: Preliminary experiments on 52 clinical OCT scans (26 normal and 26 with early-stage DR, balanced between 40–79 yr old males and females; 40 training and 12 test subjects) gave the DR detection accuracy, sensitivity, and specificity of 92%; 83%, and 100%, respectively. The 100% accuracy, sensitivity, and specificity have been obtained in the leave-one-out cross-validation test for all the 52 subjects. Conclusion: Both the quantitative and visual assessments confirmed the high accuracy of the proposed computer-assisted diagnostic system for early DR detection using the OCT retinal images.
KW - Markov–Gibbs random field (MGRF)
KW - diabetic retinopathy (DR)
KW - joint image-region-map model
KW - non-negativity-constrained autoencoder (NCAE)
KW - optical coherence tomography (OCT)
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U2 - 10.1002/MP.12071
DO - 10.1002/MP.12071
M3 - Article
C2 - 28035657
AN - SCOPUS:85016289983
SN - 0094-2405
VL - 44
SP - 914
EP - 923
JO - Medical Physics
JF - Medical Physics
IS - 3
ER -