CE-Net: Context Encoder Network for 2D Medical Image Segmentation
- Zaiwang Gu, Jun Cheng, Jiang Liu
- Computer ScienceIEEE Transactions on Medical Imaging
- 7 March 2019
Comprehensive results show that the proposed CE-Net method outperforms the original U- net method and other state-of-the-art methods for optic disc segmentation, vessel detection, lung segmentation , cell contour segmentation and retinal optical coherence tomography layer segmentation.
Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation
- H. Fu, Jun Cheng, Yanwu Xu, D. Wong, Jiang Liu, Xiaochun Cao
- Computer ScienceIEEE Transactions on Medical Imaging
- 3 January 2018
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 ScienceAnnual International Conference of the IEEE…
- 11 November 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.
Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening
The proposed segmentation methods have been evaluated in a database of 650 images with optic disc and optic cup boundaries manually marked by trained professionals and achieves areas under curve of 0.800 and 0.822 in two data sets, which is higher than other methods.
Disc-Aware Ensemble Network for Glaucoma Screening From Fundus Image
- H. Fu, Jun Cheng, Xiaochun Cao
- Computer ScienceIEEE Transactions on Medical Imaging
- 15 May 2018
A novel disc-aware ensemble network for automatic glaucoma screening is proposed, which integrates the deep hierarchical context of the global fundus image and the local optic disc region and outperforms other state-of-the-art algorithms.
CS-Net: Channel and Spatial Attention Network for Curvilinear Structure Segmentation
- Lei Mou, Yitian Zhao, Jiang Liu
- Computer ScienceInternational Conference on Medical Image…
- 13 October 2019
This work proposes a general unifying curvilinear structure segmentation network that works on different medical imaging modalities: optical coherence tomography angiography, color fundus image, and corneal confocal microscopy, and instead of the U-Net based convolutional neural network, a novel network which includes a self-attention mechanism in the encoder and decoder.
CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imaging
- Lei Mou, Yitian Zhao, Jiang Liu
- Computer ScienceMedical Image Anal.
- 15 October 2020
Sparse Dissimilarity-Constrained Coding for Glaucoma Screening
- Jun Cheng, F. Yin, D. Wong, D. Tao, Jiang Liu
- Medicine, Computer ScienceIEEE Transactions on Biomedical Engineering
- 9 January 2015
A method for cup to disc ratio (CDR) assessment using 2-D retinal fundus images using a novel sparse dissimilarity-constrained coding approach which has a great potential to be used for large-scale population-based glaucoma screening.
Dense Dilated Network With Probability Regularized Walk for Vessel Detection
- Lei Mou, Li Chen, Jun Cheng, Zaiwang Gu, Yitian Zhao, Jiang Liu
- Computer ScienceIEEE Transactions on Medical Imaging
- 26 October 2019
The proposed novel method for retinal vessel detection includes a dense dilated network to get an initial detection of the vessels and a probability regularized walk algorithm to address the fracture issue in the initial detection.
Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images
- Kang Zhou, Yuting Xiao, Shenghua Gao
- Computer ScienceEuropean Conference on Computer Vision
- 9 August 2020
This work first extracts the structure of the retinal images, then it combines both the structure features and the last layer features extracted from original health image to reconstruct the original input healthy image, and measures the difference between structure extracted from Original and the reconstructed image.
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