Multiloss Function Based Deep Convolutional Neural Network for Segmentation of Retinal Vasculature into Arterioles and Venules.
The arterioles and venules (AV) classification of retinal vasculature is considered as the first step in the development of an automated system for analysing the vasculature biomarker association with disease prognosis. Most of the existing AV classification methods depend on the accurate segmentati...
| Publicado en: | BioMed Research International pp. 1 - 18 |
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| Autores principales: | , |
| Formato: | equations & formulas pictorial protocol research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
4/14/2019
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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=135881489&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135881489 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 4/14/2019 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 135881489 135881489 135881489 10.1155/2019/4747230 135881489 ppf: 1 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Multiloss Function Based Deep Convolutional Neural Network for Segmentation of Retinal Vasculature into Arterioles and Venules. aug: au: Badawi, Sufian A. Fraz, Muhammad Moazam affil: School of Electrical Engineering and Computer Science, National University of Science and Technology, Sector H-12, Islamabad, Pakistan sug: subj: Neural Networks (Computer) Deep Learning Retinal Artery Analysis Retinal Vein Analysis Diagnosis, Eye Image Processing, Computer Assisted Human Validity ab: The arterioles and venules (AV) classification of retinal vasculature is considered as the first step in the development of an automated system for analysing the vasculature biomarker association with disease prognosis. Most of the existing AV classification methods depend on the accurate segmentation of retinal blood vessels. Moreover, the unavailability of large-scale annotated data is a major hindrance in the application of deep learning techniques for AV classification. This paper presents an encoder-decoder based fully convolutional neural network for classification of retinal vasculature into arterioles and venules, without requiring the preliminary step of vessel segmentation. An optimized multiloss function is used to learn the pixel-wise and segment-wise retinal vessel labels. The proposed method is trained and evaluated on DRIVE, AVRDB, and a newly created AV classification dataset; and it attains 96%, 98%, and 97% accuracy, respectively. The new AV classification dataset is comprised of 700 annotated retinal images, which will offer the researchers a benchmark to compare their AV classification results. pubtype: Academic Journal doctype: equations & formulas pictorial protocol research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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