FFU-Net: Feature Fusion U-Net for Lesion Segmentation of Diabetic Retinopathy.
Diabetic retinopathy is one of the main causes of blindness in human eyes, and lesion segmentation is an important basic work for the diagnosis of diabetic retinopathy. Due to the small lesion areas scattered in fundus images, it is laborious to segment the lesion of diabetic retinopathy effectively...
| Publicado en: | BioMed Research International Vol. 2021; pp. 1 - 13 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
1/2/2021
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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=148462462&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148462462 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 1/2/2021 vid: 2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 148462462 148462462 148462462 10.1155/2021/6644071 148462462 ppf: 1 ppct: 12 formats: fmt: @attributes: type: P tig: atl: FFU-Net: Feature Fusion U-Net for Lesion Segmentation of Diabetic Retinopathy. aug: au: Yifei Xu Zhuming Zhou Xiao Li Nuo Zhang Meizi Zhang Pingping Wei affil: School of Software, Xi'an Jiaotong University, 710054, Xi'an, Shaanxi, China sug: subj: Diabetic Retinopathy Etiology Blindness Risk Factors Image Processing, Computer Assisted Methods Human Spatial Perception Retinal Diseases Diagnostic Imaging Methods Ablation Techniques Descriptive Statistics Deep Learning Methods ab: Diabetic retinopathy is one of the main causes of blindness in human eyes, and lesion segmentation is an important basic work for the diagnosis of diabetic retinopathy. Due to the small lesion areas scattered in fundus images, it is laborious to segment the lesion of diabetic retinopathy effectively with the existing U-Net model. In this paper, we proposed a new lesion segmentation model named FFU-Net (Feature Fusion U-Net) that enhances U-Net from the following points. Firstly, the pooling layer in the network is replaced with a convolutional layer to reduce spatial loss of the fundus image. Then, we integrate multiscale feature fusion (MSFF) block into the encoders which helps the network to learn multiscale features efficiently and enrich the information carried with skip connection and lower-resolution decoder by fusing contextual channel attention (CCA) models. Finally, in order to solve the problems of data imbalance and misclassification, we present a Balanced Focal Loss function. In the experiments on benchmark dataset IDRID, we make an ablation study to verify the effectiveness of each component and compare FFU-Net against several state-of-the-art models. In comparison with baseline U-Net, FFU-Net improves the segmentation performance by 11.97%, 10.68%, and 5.79% on metrics SEN, IOU, and DICE, respectively. The quantitative and qualitative results demonstrate the superiority of our FFU-Net in the task of lesion segmentation of diabetic retinopathy. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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