Utilizing Transfer Learning and a Customized Loss Function for Optic Disc Segmentation from Retinal Images

  title={Utilizing Transfer Learning and a Customized Loss Function for Optic Disc Segmentation from Retinal Images},
  author={Abdullah Sarhan and Ali Al-Khaz'Aly and Adam Gorner and Andrew Swift and Jon G. Rokne and Reda Alhajj and Andrew Crichton},
Accurate segmentation of the optic disc from a retinal image is vital to extracting retinal features that may be highly correlated with retinal conditions such as glaucoma. In this paper, we propose a deep-learning based approach capable of segmenting the optic disc given a high-precision retinal fundus image. Our approach utilizes a UNET-based model with a VGG16 encoder trained on the ImageNet dataset. This study can be distinguished from other studies in the customization made for the VGG16… 
1 Citations
Joint optic disc and cup segmentation based on multi-scale feature analysis and attention pyramid architecture for glaucoma screening
A unified convolutional neural network, named ResFPN-Net, which learns the boundary feature and the inner relation between OD and OC for automatic segmentation and is effective in analysing fundus images for glaucoma screening and can be applied in other relative biomedical image segmentation applications.


Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation
A deep learning architecture, named M-Net, is proposed, which solves the OD and OC segmentation jointly in a one-stage multi-label system and introduces the polar transformation, which provides the representation of the original image in the polar coordinate system.
ORIGA-light: An online retinal fundus image database for glaucoma analysis and research
  • Zhuo Zhang, F. Yin, T. Wong
  • Medicine, Computer Science
    2010 Annual International Conference of the IEEE Engineering in Medicine and Biology
  • 2010
An online depository, ORIGA-light, is presented, which aims to share clinical groundtruth retinal images with the public; provide open access for researchers to benchmark their computer-aided segmentation algorithms; and quantified objective benchmarking method, focusing on optic disc and cup segmentation and Cup-to-Disc Ratio.
Optic Disc and Cup Segmentation Based on Deep Convolutional Generative Adversarial Networks
This paper proposes GL-Net, a multi-label DCNN model that combines the generative adversarial networks and uses transfer learning and data augmentation to alleviate the problem of insufficient data and over-fitting of the model during training.
Patch-Based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation
A novel patch-based output space adversarial learning framework to jointly and robustly segment the optic disc and OC from different fundus image datasets and achieves the first place in the OD and OC segmentation tasks in the MICCAI 2018 Retinal Fundus Glaucoma Challenge.
Retinal fundus images for glaucoma analysis: the RIGA dataset
A de-identified dataset of retinal fundus images for glaucoma analysis (RIGA) was derived from three sources for a total of 750 images and will be made available to the research community in order to crowd source other analysis from other research groups in to develop, validate and implement analysis algorithms appropriate for tele-glauca assessment.
Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentation
The proposed BEAL frame-work utilizes the adversarial learning to encourage the boundary prediction and mask probability entropy map of the target domain to be similar to the source ones, generating more accurate boundaries and suppressing the high uncertainty predictions of OD and OC segmentation.
Drishti-GS: Retinal image dataset for optic nerve head(ONH) segmentation
A comprehensive dataset of retinal images which include both normal and glaucomatous eyes and manual segmentations from multiple human experts is presented and area and boundary-based evaluation measures are presented to evaluate a method on various aspects relevant to the problem ofglaucoma assessment.