eprintid: 12705
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/27/05
datestamp: 2023-11-10 03:27:16
lastmod: 2023-11-10 03:27:16
status_changed: 2023-11-10 01:49:19
type: article
metadata_visibility: show
creators_name: Alsaih, K.
creators_name: Yusoff, M.Z.
creators_name: Tang, T.B.
creators_name: Faye, I.
creators_name: Mériaudeau, F.
title: Deep learning architectures analysis for age-related macular degeneration segmentation on optical coherence tomography scans
ispublished: pub
keywords: Convolutional neural networks; Image segmentation; Learning systems; Medical imaging; Object detection; Ophthalmology; Optical tomography; Tomography, Age-related macular degeneration; Depth information; Developed countries; Human performance; Intra-retinal fluids; Learning architectures; Machine learning approaches; Similarity coefficients, Deep learning, age related macular degeneration; Article; B scan; clinical article; comparative study; convolutional neural network; deep learning; diagnostic accuracy; fully convolutional network; gated recurrent unit network; human; image segmentation; imaging algorithm; internal limiting membrane; intraretinal fluid; liquid; retinal pigment epithelium; seg net algorithm; spectral domain optical coherence tomography; two-dimensional imaging; aged; diagnostic imaging; macular degeneration; optical coherence tomography; retina, Aged; Deep Learning; Humans; Macular Degeneration; Neural Networks, Computer; Retina; Tomography, Optical Coherence
note: cited By 26
abstract: Background and objectives: Aged people usually are more to be diagnosed with retinal diseases in developed countries. Retinal capillaries leakage into the retina swells and causes an acute vision loss, which is called age-related macular degeneration (AMD). The disease can not be adequately diagnosed solely using fundus images as depth information is not available. The variations in retina volume assist in monitoring ophthalmological abnormalities. Therefore, high-fidelity AMD segmentation in optical coherence tomography (OCT) imaging modality has raised the attention of researchers as well as those of the medical doctors. Many methods across the years encompassing machine learning approaches and convolutional neural networks (CNN) strategies have been proposed for object detection and image segmentation. Methods: In this paper, we analyze four wide-spread deep learning models designed for the segmentation of three retinal fluids outputting dense predictions in the RETOUCH challenge data. We aim to demonstrate how a patch-based approach could push the performance for each method. Besides, we also evaluate the methods using the OPTIMA challenge dataset for generalizing network performance. The analysis is driven into two sections: the comparison between the four approaches and the significance of patching the images. Results: The performance of networks trained on the RETOUCH dataset is higher than human performance. The analysis further generalized the performance of the best network obtained by fine-tuning it and achieved a mean Dice similarity coefficient (DSC) of 0.85. Out of the three types of fluids, intraretinal fluid (IRF) is more recognized, and the highest DSC value of 0.922 is achieved using Spectralis dataset. Additionally, the highest average DSC score is 0.84, which is achieved by PaDeeplabv3+ model using Cirrus dataset. Conclusions: The proposed method segments the three fluids in the retina with high DSC value. Fine-tuning the networks trained on the RETOUCH dataset makes the network perform better and faster than training from scratch. Enriching the networks with inputting a variety of shapes by extracting patches helped to segment the fluids better than using a full image. © 2020
date: 2020
publisher: Elsevier Ireland Ltd
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085733192&doi=10.1016%2fj.cmpb.2020.105566&partnerID=40&md5=bc35e93c0206e32404edab90f5871903
id_number: 10.1016/j.cmpb.2020.105566
full_text_status: none
publication: Computer Methods and Programs in Biomedicine
volume: 195
refereed: TRUE
issn: 01692607
citation:   Alsaih, K. and Yusoff, M.Z. and Tang, T.B. and Faye, I. and Mériaudeau, F.  (2020) Deep learning architectures analysis for age-related macular degeneration segmentation on optical coherence tomography scans.  Computer Methods and Programs in Biomedicine, 195.   ISSN 01692607