Cargando…

Deep Learning-Based Detection of Pigment Signs for Analysis and Diagnosis of Retinitis Pigmentosa

Ophthalmological analysis plays a vital role in the diagnosis of various eye diseases, such as glaucoma, retinitis pigmentosa (RP), and diabetic and hypertensive retinopathy. RP is a genetic retinal disorder that leads to progressive vision degeneration and initially causes night blindness. Currentl...

Descripción completa

Detalles Bibliográficos
Autores principales: Arsalan, Muhammad, Baek, Na Rae, Owais, Muhammad, Mahmood, Tahir, Park, Kang Ryoung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7349531/
https://www.ncbi.nlm.nih.gov/pubmed/32570943
http://dx.doi.org/10.3390/s20123454
_version_ 1783557076292730880
author Arsalan, Muhammad
Baek, Na Rae
Owais, Muhammad
Mahmood, Tahir
Park, Kang Ryoung
author_facet Arsalan, Muhammad
Baek, Na Rae
Owais, Muhammad
Mahmood, Tahir
Park, Kang Ryoung
author_sort Arsalan, Muhammad
collection PubMed
description Ophthalmological analysis plays a vital role in the diagnosis of various eye diseases, such as glaucoma, retinitis pigmentosa (RP), and diabetic and hypertensive retinopathy. RP is a genetic retinal disorder that leads to progressive vision degeneration and initially causes night blindness. Currently, the most commonly applied method for diagnosing retinal diseases is optical coherence tomography (OCT)-based disease analysis. In contrast, fundus imaging-based disease diagnosis is considered a low-cost diagnostic solution for retinal diseases. This study focuses on the detection of RP from the fundus image, which is a crucial task because of the low quality of fundus images and non-cooperative image acquisition conditions. Automatic detection of pigment signs in fundus images can help ophthalmologists and medical practitioners in diagnosing and analyzing RP disorders. To accurately segment pigment signs for diagnostic purposes, we present an automatic RP segmentation network (RPS-Net), which is a specifically designed deep learning-based semantic segmentation network to accurately detect and segment the pigment signs with fewer trainable parameters. Compared with the conventional deep learning methods, the proposed method applies a feature enhancement policy through multiple dense connections between the convolutional layers, which enables the network to discriminate between normal and diseased eyes, and accurately segment the diseased area from the background. Because pigment spots can be very small and consist of very few pixels, the RPS-Net provides fine segmentation, even in the case of degraded images, by importing high-frequency information from the preceding layers through concatenation inside and outside the encoder-decoder. To evaluate the proposed RPS-Net, experiments were performed based on 4-fold cross-validation using the publicly available Retinal Images for Pigment Signs (RIPS) dataset for detection and segmentation of retinal pigments. Experimental results show that RPS-Net achieved superior segmentation performance for RP diagnosis, compared with the state-of-the-art methods.
format Online
Article
Text
id pubmed-7349531
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-73495312020-07-14 Deep Learning-Based Detection of Pigment Signs for Analysis and Diagnosis of Retinitis Pigmentosa Arsalan, Muhammad Baek, Na Rae Owais, Muhammad Mahmood, Tahir Park, Kang Ryoung Sensors (Basel) Article Ophthalmological analysis plays a vital role in the diagnosis of various eye diseases, such as glaucoma, retinitis pigmentosa (RP), and diabetic and hypertensive retinopathy. RP is a genetic retinal disorder that leads to progressive vision degeneration and initially causes night blindness. Currently, the most commonly applied method for diagnosing retinal diseases is optical coherence tomography (OCT)-based disease analysis. In contrast, fundus imaging-based disease diagnosis is considered a low-cost diagnostic solution for retinal diseases. This study focuses on the detection of RP from the fundus image, which is a crucial task because of the low quality of fundus images and non-cooperative image acquisition conditions. Automatic detection of pigment signs in fundus images can help ophthalmologists and medical practitioners in diagnosing and analyzing RP disorders. To accurately segment pigment signs for diagnostic purposes, we present an automatic RP segmentation network (RPS-Net), which is a specifically designed deep learning-based semantic segmentation network to accurately detect and segment the pigment signs with fewer trainable parameters. Compared with the conventional deep learning methods, the proposed method applies a feature enhancement policy through multiple dense connections between the convolutional layers, which enables the network to discriminate between normal and diseased eyes, and accurately segment the diseased area from the background. Because pigment spots can be very small and consist of very few pixels, the RPS-Net provides fine segmentation, even in the case of degraded images, by importing high-frequency information from the preceding layers through concatenation inside and outside the encoder-decoder. To evaluate the proposed RPS-Net, experiments were performed based on 4-fold cross-validation using the publicly available Retinal Images for Pigment Signs (RIPS) dataset for detection and segmentation of retinal pigments. Experimental results show that RPS-Net achieved superior segmentation performance for RP diagnosis, compared with the state-of-the-art methods. MDPI 2020-06-18 /pmc/articles/PMC7349531/ /pubmed/32570943 http://dx.doi.org/10.3390/s20123454 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Arsalan, Muhammad
Baek, Na Rae
Owais, Muhammad
Mahmood, Tahir
Park, Kang Ryoung
Deep Learning-Based Detection of Pigment Signs for Analysis and Diagnosis of Retinitis Pigmentosa
title Deep Learning-Based Detection of Pigment Signs for Analysis and Diagnosis of Retinitis Pigmentosa
title_full Deep Learning-Based Detection of Pigment Signs for Analysis and Diagnosis of Retinitis Pigmentosa
title_fullStr Deep Learning-Based Detection of Pigment Signs for Analysis and Diagnosis of Retinitis Pigmentosa
title_full_unstemmed Deep Learning-Based Detection of Pigment Signs for Analysis and Diagnosis of Retinitis Pigmentosa
title_short Deep Learning-Based Detection of Pigment Signs for Analysis and Diagnosis of Retinitis Pigmentosa
title_sort deep learning-based detection of pigment signs for analysis and diagnosis of retinitis pigmentosa
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7349531/
https://www.ncbi.nlm.nih.gov/pubmed/32570943
http://dx.doi.org/10.3390/s20123454
work_keys_str_mv AT arsalanmuhammad deeplearningbaseddetectionofpigmentsignsforanalysisanddiagnosisofretinitispigmentosa
AT baeknarae deeplearningbaseddetectionofpigmentsignsforanalysisanddiagnosisofretinitispigmentosa
AT owaismuhammad deeplearningbaseddetectionofpigmentsignsforanalysisanddiagnosisofretinitispigmentosa
AT mahmoodtahir deeplearningbaseddetectionofpigmentsignsforanalysisanddiagnosisofretinitispigmentosa
AT parkkangryoung deeplearningbaseddetectionofpigmentsignsforanalysisanddiagnosisofretinitispigmentosa