文章詳目資料
Journal of Computers EIMEDLINEScopus
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| 篇名 | Res-UNet Based Optic Disk Segmentation in Retinal Image |
|---|---|
| 卷期 | 31:3 |
| 作者 | Jia-Wen Lin 、 Xiang-Wen Liao 、 Lun Yu 、 Jeng-Shyang Pan |
| 頁次 | 183-194 |
| 關鍵字 | morphology method 、 optic disk segmentation 、 residual learning 、 retinal image 、 UNet 、 EI 、 MEDLINE 、 Scopus |
| 出刊日期 | 202006 |
| DOI | 10.3966/199115992020063103014 |
中文摘要
英文摘要
Automatic optic disk (OD) segmentation is an important tool for early detection of eye diseases. In this article, we proposed a Res-UNet network by applying residual learning module and other improvements in U-Net for optic disk segmentation in retinal image. Since training data available is insufficient, we enlarge the data set by generating data pieces. Res-UNet is then trained to classify each pixel of the input retinal image. Finally, the predicted probability map is further post-processed with morphological technique to get final OD segmentation result. Experiments on the public DRISHTI-GS data set including comparison with the best known methods show that the proposed model outperforms most existing methods on several metrics.
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