• Corpus ID: 219573852

W-net: Simultaneous segmentation of multi-anatomical retinal structures using a multi-task deep neural network

  title={W-net: Simultaneous segmentation of multi-anatomical retinal structures using a multi-task deep neural network},
  author={Hongwei Zhao and Chengtao Peng and Lei Liu and Bin Li},
Segmentation of multiple anatomical structures is of great importance in medical image analysis. In this study, we proposed a $\mathcal{W}$-net to simultaneously segment both the optic disc (OD) and the exudates in retinal images based on the multi-task learning (MTL) scheme. We introduced a class-balanced loss and a multi-task weighted loss to alleviate the imbalanced problem and to improve the robustness and generalization property of the $\mathcal{W}$-net. We demonstrated the effectiveness… 



Deep Retinal Image Segmentation: A FCN-Based Architecture with Short and Long Skip Connections for Retinal Image Segmentation

The paper presents a new formulation of fully Convolutional Neural Networks (FCNs) that allows accurate segmentation of the retinal images that achieves strong performance and significantly outperforms the-state-of-the-art.

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.

U-Net: Convolutional Networks for Biomedical Image Segmentation

It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.

Automatic Optic Disk and Cup Segmentation of Fundus Images Using Deep Learning

A deep learning system using fully convolutional neural networks (FCN) to perform automatic segmentation of optic disk and cup regions in fundus images is presented, and various strategies on how to leverage multiple doctor annotations and prioritize pixels belonging to different regions while training the neural network are discussed.

Using deep learning for robustness to parapapillary atrophy in optic disc segmentation

This paper proposes to use a deep neural network for OD segmentation which can learn features to distinguish PPA from OD using simple image intensity based features and has the least mean overlapping error.

Deep Retinal Image Understanding

Deep Retinal Image Understanding is presented, a unified framework of retinal image analysis that provides both retinal vessel and optic disc segmentation and shows super-human performance, that is, it shows results more consistent with a gold standard than a second human annotator used as control.

Accurate and reliable segmentation of the optic disc in digital fundus images

This procedure provides separation of vascular information and accurate inpainting of vessel-removed images, symmetry-based optic disc localization, and fitting of incrementally complex contour models at increasing resolutions using information related to inpainted images and vessel masks.

Optic Disk and Cup Segmentation From Monocular Color Retinal Images for Glaucoma Assessment

An automatic OD parameterization technique based on segmented OD and cup regions obtained from monocular retinal images and a novel cup segmentation method which is based on anatomical evidence such as vessel bends at the cup boundary, considered relevant by glaucoma experts are presented.

Localization and segmentation of optic disc in retinal images using circular Hough transform and grow-cut algorithm

A robust methodology for optic disc detection and boundary segmentation is presented, which can be seen as the preliminary step in the development of a computer-assisted diagnostic system for glaucoma in retinal images.