Automated segmentation of the optic disc from fundus images using an asymmetric deep learning network
作者：
Highlights：
• A novel deep learning network was proposed based on the classical U-Net model to accurately segment the optic disc from colour fundus images.
• A sub-network and a decoding convolutional block were introduced to provide additional key features and highlight the morphological changes of the target objects in convolutional feature maps.
• Experiment results on both the global field-of-view fundus images and their local disc versions from the MESSIDOR, ORIGA, and REFUGE datasets demonstrated that the developed network achieved promising performance and outperformed some existing segmentation networks.
摘要
•A novel deep learning network was proposed based on the classical U-Net model to accurately segment the optic disc from colour fundus images.•A sub-network and a decoding convolutional block were introduced to provide additional key features and highlight the morphological changes of the target objects in convolutional feature maps.•Experiment results on both the global field-of-view fundus images and their local disc versions from the MESSIDOR, ORIGA, and REFUGE datasets demonstrated that the developed network achieved promising performance and outperformed some existing segmentation networks.
论文关键词：Segmentation,Colour fundus images,Optic disc,Deep learning,U-Net
论文评审过程：Received 16 April 2020, Revised 22 November 2020, Accepted 18 December 2020, Available online 5 January 2021, Version of Record 9 January 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2020.107810
