RetFluidNet: Retinal Fluid Segmentation for SD-OCT Images Using Convolutional Neural Network.
Age-related macular degeneration (AMD) is one of the leading causes of irreversible blindness and is characterized by fluid-related accumulations such as intra-retinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED). Spectral-domain optical coherence tomography (SD-OCT)...
| Publicado en: | Journal of Digital Imaging Vol. 34; no. 3; pp. 691 - 705 |
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| Autores principales: | , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151702168&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151702168 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2021 vid: 34 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 151702168 150631281 151702168 151702168 10.1007/s10278-021-00459-w 151702168 ppf: 691 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: RetFluidNet: Retinal Fluid Segmentation for SD-OCT Images Using Convolutional Neural Network. aug: au: Sappa, Loza Bekalo Okuwobi, Idowu Paul Li, Mingchao Zhang, Yuhan Xie, Sha Yuan, Songtao Chen, Qiang affil: School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, 210094, Nanjing, China sug: subj: Exudates and Transudates Physiopathology Retina Radiography Tomography, Optical Coherence Methods Retinal Diseases Classification Neural Networks (Computer) Models, Statistical Human Macular Degeneration Diagnosis Retinal Diseases Pathology Diagnosis, Eye Methods Technology Retinal Diseases Diagnosis Sensitivity and Specificity Descriptive Statistics Automation Early Diagnosis After Care ab: Age-related macular degeneration (AMD) is one of the leading causes of irreversible blindness and is characterized by fluid-related accumulations such as intra-retinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED). Spectral-domain optical coherence tomography (SD-OCT) is the primary modality used to diagnose AMD, yet it does not have algorithms that directly detect and quantify the fluid. This work presents an improved convolutional neural network (CNN)-based architecture called RetFluidNet to segment three types of fluid abnormalities from SD-OCT images. The model assimilates different skip-connect operations and atrous spatial pyramid pooling (ASPP) to integrate multi-scale contextual information; thus, achieving the best performance. This work also investigates between consequential and comparatively inconsequential hyperparameters and skip-connect techniques for fluid segmentation from the SD-OCT image to indicate the starting choice for future related researches. RetFluidNet was trained and tested on SD-OCT images from 124 patients and achieved an accuracy of 80.05%, 92.74%, and 95.53% for IRF, PED, and SRF, respectively. RetFluidNet showed significant improvement over competitive works to be clinically applicable in reasonable accuracy and time efficiency. RetFluidNet is a fully automated method that can support early detection and follow-up of AMD. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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