Tags

Type your tag names separated by a space and hit enter

Early diabetic retinopathy diagnosis based on local retinal blood vessel analysis in optical coherence tomography angiography (OCTA) images.
Med Phys. 2018 Oct; 45(10):4582-4599.MP

Abstract

PURPOSE

This paper introduces a new computer-aided diagnosis (CAD) system for detecting early-stage diabetic retinopathy (DR) using optical coherence tomography angiography (OCTA) images.

METHODS

The proposed DR-CAD system is based on the analysis of new local features that describe both the appearance and retinal structure in OCTA images. It starts with a new segmentation approach that has the ability to extract the blood vessels from superficial and deep retinal OCTA maps. The high capability of our segmentation approach stems from using a joint Markov-Gibbs random field stochastic model integrating a 3D spatial statistical model with a first-order appearance model of the blood vessels. Following the segmentation step, three new local features are estimated from the segmented vessels and the foveal avascular zone (FAZ): (a) vessels density, (b) blood vessel calibre, and (c) width of the FAZ. To distinguish mild DR patients from normal cases, the estimated three features are used to train and test a support vector machine (SVM) classifier with the radial basis function (RBF) kernel.

RESULTS

On a cohort of 105 subjects, the presented DR-CAD system demonstrated an overall accuracy (ACC) of 94.3%, a sensitivity of 97.9%, a specificity of 87.0%, the area under the curve (AUC) of 92.4%, and a Dice similarity coefficient (DSC) of 95.8%. This in turn demonstrates the promise of the proposed CAD system as a supplemental tool for early detection of DR.

CONCLUSION

We developed a new DR-CAD system that is capable of diagnosing DR in its early stage. The proposed system is based on extracting three different features from the segmented OCTA images, which reflect the changes in the retinal vasculature network.

Authors+Show Affiliations

Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt. Bioengineering Department, University of Louisville, Louisville, KY40292, USA.Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt. Bioengineering Department, University of Louisville, Louisville, KY40292, USA.Electronics and Communications Engineering Department, Mansoura University, Mansoura, Egypt.Electrical and Computer Engineering Department, Abu Dhabi University, Abu Dhabi, UAE.Eye Institute at Cleveland Clinic, Abu Dhabi, UAE.Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt.Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt.Ophthalmology and Visual Sciences Department, School of Medicine, University of Louisville, Louisville, KY, USA.Department of Ophthalmology and Visual Sciences, University of Massachusetts Medical School, Worcester, MA, USA.Bioengineering Department, University of Louisville, Louisville, KY40292, USA.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

30144102

Citation

Eladawi, Nabila, et al. "Early Diabetic Retinopathy Diagnosis Based On Local Retinal Blood Vessel Analysis in Optical Coherence Tomography Angiography (OCTA) Images." Medical Physics, vol. 45, no. 10, 2018, pp. 4582-4599.
Eladawi N, Elmogy M, Khalifa F, et al. Early diabetic retinopathy diagnosis based on local retinal blood vessel analysis in optical coherence tomography angiography (OCTA) images. Med Phys. 2018;45(10):4582-4599.
Eladawi, N., Elmogy, M., Khalifa, F., Ghazal, M., Ghazi, N., Aboelfetouh, A., Riad, A., Sandhu, H., Schaal, S., & El-Baz, A. (2018). Early diabetic retinopathy diagnosis based on local retinal blood vessel analysis in optical coherence tomography angiography (OCTA) images. Medical Physics, 45(10), 4582-4599. https://doi.org/10.1002/mp.13142
Eladawi N, et al. Early Diabetic Retinopathy Diagnosis Based On Local Retinal Blood Vessel Analysis in Optical Coherence Tomography Angiography (OCTA) Images. Med Phys. 2018;45(10):4582-4599. PubMed PMID: 30144102.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Early diabetic retinopathy diagnosis based on local retinal blood vessel analysis in optical coherence tomography angiography (OCTA) images. AU - Eladawi,Nabila, AU - Elmogy,Mohammed, AU - Khalifa,Fahmi, AU - Ghazal,Mohammed, AU - Ghazi,Nicola, AU - Aboelfetouh,Ahmed, AU - Riad,Alaa, AU - Sandhu,Harpal, AU - Schaal,Shlomit, AU - El-Baz,Ayman, Y1 - 2018/09/19/ PY - 2018/07/17/received PY - 2018/08/13/revised PY - 2018/08/15/accepted PY - 2018/8/26/pubmed PY - 2018/11/20/medline PY - 2018/8/26/entrez KW - early diabetic retinopathy (DR) diagnosis KW - local retinal blood vessels analysis KW - optical coherence tomography angiography (OCTA) KW - support vector machine (SVM), foveal avascular zone (FAZ) SP - 4582 EP - 4599 JF - Medical physics JO - Med Phys VL - 45 IS - 10 N2 - PURPOSE: This paper introduces a new computer-aided diagnosis (CAD) system for detecting early-stage diabetic retinopathy (DR) using optical coherence tomography angiography (OCTA) images. METHODS: The proposed DR-CAD system is based on the analysis of new local features that describe both the appearance and retinal structure in OCTA images. It starts with a new segmentation approach that has the ability to extract the blood vessels from superficial and deep retinal OCTA maps. The high capability of our segmentation approach stems from using a joint Markov-Gibbs random field stochastic model integrating a 3D spatial statistical model with a first-order appearance model of the blood vessels. Following the segmentation step, three new local features are estimated from the segmented vessels and the foveal avascular zone (FAZ): (a) vessels density, (b) blood vessel calibre, and (c) width of the FAZ. To distinguish mild DR patients from normal cases, the estimated three features are used to train and test a support vector machine (SVM) classifier with the radial basis function (RBF) kernel. RESULTS: On a cohort of 105 subjects, the presented DR-CAD system demonstrated an overall accuracy (ACC) of 94.3%, a sensitivity of 97.9%, a specificity of 87.0%, the area under the curve (AUC) of 92.4%, and a Dice similarity coefficient (DSC) of 95.8%. This in turn demonstrates the promise of the proposed CAD system as a supplemental tool for early detection of DR. CONCLUSION: We developed a new DR-CAD system that is capable of diagnosing DR in its early stage. The proposed system is based on extracting three different features from the segmented OCTA images, which reflect the changes in the retinal vasculature network. SN - 2473-4209 UR - https://www.unboundmedicine.com/medline/citation/30144102/Early_diabetic_retinopathy_diagnosis_based_on_local_retinal_blood_vessel_analysis_in_optical_coherence_tomography_angiography__OCTA__images_ L2 - https://doi.org/10.1002/mp.13142 DB - PRIME DP - Unbound Medicine ER -