Multi-scale Bottleneck Residual Network for Retinal Vessel Segmentation.
Precise segmentation of retinal vessels is crucial for the prevention and diagnosis of ophthalmic diseases. In recent years, deep learning has shown outstanding performance in retinal vessel segmentation. Many scholars are dedicated to studying retinal vessel segmentation methods based on color fund...
| Publicado en: | Journal of Medical Systems Vol. 47; no. 1; pp. 1 - 18 |
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| Autores principales: | , , , , |
| Formato: | algorithm diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
9/30/2023
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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=172971129&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 172971129 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 9/30/2023 vid: 47 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 172971129 172971129 172971129 10.1007/s10916-023-01992-7 172971129 ppf: 1 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Multi-scale Bottleneck Residual Network for Retinal Vessel Segmentation. aug: au: Li, Peipei Qiu, Zhao Zhan, Yuefu Chen, Huajing Yuan, Sheng affil: https://ror.org/03q648j11 School of Computer Science and Technology, Hainan University, 570228, Haikou, China sug: subj: Retina Pathology Eye Diseases Diagnosis Eye Diseases Prevention and Control Image Processing, Computer Assisted Ophthalmoscopy Deep Learning Utilization Lasers Utilization Digital Imaging Human Funding Source Ablation Techniques Attention ab: Precise segmentation of retinal vessels is crucial for the prevention and diagnosis of ophthalmic diseases. In recent years, deep learning has shown outstanding performance in retinal vessel segmentation. Many scholars are dedicated to studying retinal vessel segmentation methods based on color fundus images, but the amount of research works on Scanning Laser Ophthalmoscopy (SLO) images is very scarce. In addition, existing SLO image segmentation methods still have difficulty in balancing accuracy and model parameters. This paper proposes a SLO image segmentation model based on lightweight U-Net architecture called MBRNet, which solves the problems in the current research through Multi-scale Bottleneck Residual (MBR) module and attention mechanism. Concretely speaking, the MBR module expands the receptive field of the model at a relatively low computational cost and retains more detailed information. Attention Gate (AG) module alleviates the disturbance of noise so that the network can concentrate on vascular characteristics. Experimental results on two public SLO datasets demonstrate that by comparison to existing methods, the MBRNet has better segmentation performance with relatively few parameters. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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