Dual-stage deep learning framework for pigment epithelium detachment segmentation in polypoidal choroidal vasculopathy

Abstract

Worldwide, polypoidal choroidal vasculopathy (PCV) is a common visionthreatening exudative maculopathy, and pigment epithelium detachment (PED) is an important clinical characteristic. Thus, precise and efficient PED segmentation is necessary for PCV clinical diagnosis and treatment. We propose a dual-stage learning framework via deep neural networks (DNN) for automated PED segmentation in PCV patients to avoid issues associated with manual PED segmentation (subjectivity, manual segmentation errors, and high time consumption).The optical coherence tomography scans of fifty patients were quantitatively evaluated with different algorithms and clinicians. Dual-stage DNN outperformed existing PED segmentation methods for all segmentation accuracy parameters, including true positive volume fraction (85.74 ± 8.69%), dice similarity coefficient (85.69 ± 8.08%), positive predictive value (86.02 ± 8.99%) and false positive volume fraction (0.38 ± 0.18%). Dual-stage DNN achieves accurate PED quantitative information, works with multiple types of PEDs and agrees well with manual delineation, suggesting that it is a potential automated assistant for PCV management. 467-473 (1996). 6. J. D. Gass, S. B. Bressler, L. Akduman, J. Olk, P. J. Caskey, and L. E. Zimmerman, "Bilateral idiopathic multifocal retinal pigment epithelium detachments in otherwise healthy middle-aged adults: a clinicopathologic study," Retina 25(3), 304-310 (2005). 7. U. Schmidt-Erfurth, S. M. Waldstein, G. G. Deak, M. Kundi, and C. Simader, "Pigment epithelial detachment followed by retinal cystoid degeneration leads to vision loss in treatment of neovascular age-related macular degeneration," Ophthalmology 122(4), 822-832 (2015). 8. N. Nagai, M. Suzuki, A. Uchida, T. Kurihara, M. Kamoshita, S. Minami, H. Shinoda, K. Tsubota, and Y.Ozawa, "Non-responsiveness to intravitreal aflibercept treatment in neovascular age-related macular degeneration: implications of serous pigment epithelial detachment," Sci. 289-296 (2015). 99(3),

Deep learning in ophthalmology: The technical and clinical considerations

“…More recently, some groups have extended their models to perform segmentation of pigment epithelium detachment (PED), the formation of a potential space between the retinal pigment epithelium (RPE) and Bruch's membrane. 196,197 Schmidt-Erfurth et al have reported the correlation of PED metrics with visual acuity in patients with neovascular AMD using a DL-based system. 198 Detailed description and validation of this PED segmentation approach has not yet been published but it appears to treat PED as a single entity rather than a range of specific subtypes.…”

Retinal Boundary Segmentation in Stargardt Disease Optical Coherence Tomography Images Using Automated Deep Learning

“… 9 – 14 More recently, machine learning and deep learning methods have been used, including support vector machines, 15 , 16 random forest classifiers, 17 patch-based classification with convolutional neural networks 18 – 22 or recurrent neural networks, 20 , 22 semantic segmentation with fully convolutional (encoder–decoder) networks, 22 – 26 and other deep learning methods. 27 – 30 Importantly, some of these methods have been applied to OCT images from patients with age-related macular degeneration, 18 , 20 , 24 , 27 diabetic retinopathy, 11 , 25 macular telangiectasia type 2, 29 diabetic macular oedema, 13 , 23 , 24 pigment epithelium detachment, 28 glaucoma, 15 , 30 multiple sclerosis 17 , 26 retinitis pigmentosa, 31 and neurodegenerative diseases. 32 These diseases are characterized by variable thinning of the inner retinal layers (e.g., glaucoma and multiple sclerosis), thickening or cystic changes in the nuclear layers (e.g., macular telangiectasia type 2 and diabetic retinopathy) or focal disruption of the retinal pigment epithelium (RPE, e.g., age-related macular degeneration, macular telangiectasia, and pigment epithelium detachment).…”

Application of machine learning in ophthalmic imaging modalities

“…Notably, the algorithm detected all urgent referral cases within the patient cohort [82]. With the development of DL, some researchers have extended their algorithms to perform segmentation of pigment epithelium detachment, fluid and vessels [83][84][85].…”