Optic Disc Segmentation

7 papers with code • 7 benchmarks • 3 datasets

This task has no description! Would you like to contribute one?

Most implemented papers

CE-Net: Context Encoder Network for 2D Medical Image Segmentation

HzFu/MNet_DeepCDR 7 Mar 2019

In this paper, we propose a context encoder network (referred to as CE-Net) to capture more high-level information and preserve spatial information for 2D medical image segmentation.

Deep Retinal Image Understanding

PB17151764/2020UM-Summer-Research 5 Sep 2016

This paper presents Deep Retinal Image Understanding (DRIU), a unified framework of retinal image analysis that provides both retinal vessel and optic disc segmentation.

Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation

xmengli999/tcsm 28 Feb 2019

In this paper, we present a novel semi-supervised method for medical image segmentation, where the network is optimized by the weighted combination of a common supervised loss for labeled inputs only and a regularization loss for both labeled and unlabeled data.

Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation

utkuozbulak/adaptive-segmentation-mask-attack 30 Jul 2019

Given that a large portion of medical imaging problems are effectively segmentation problems, we analyze the impact of adversarial examples on deep learning-based image segmentation models.

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

AbdullahSarhan/ACCVDiscSegmentation 1 Oct 2020

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.

Medical Image Segmentation Using Squeeze-and-Expansion Transformers

askerlee/segtran 20 May 2021

Medical image segmentation is important for computer-aided diagnosis.

U-Net with Hierarchical Bottleneck Attention for Landmark Detection in Fundus Images of the Degenerated Retina

bionicvisionlab/2021-HBA-U-Net 9 Jul 2021

The network consists of a novel bottleneck attention block that combines and refines self-attention, channel attention, and relative-position attention to highlight retinal abnormalities that may be important for fovea and OD segmentation in the degenerated retina.