Joint optic disc and optic cup segmentation based on boundary prior and adversarial learning

2021 ◽  
Author(s):  
Ling Luo ◽  
Dingyu Xue ◽  
Feng Pan ◽  
Xinglong Feng
Keyword(s):  
Optic Disc ◽  
Adversarial Learning ◽  
Optic Cup

NENet: Nested EfficientNet and adversarial learning for joint optic disc and cup segmentation

2021 ◽  
Vol 74 ◽  
pp. 102253
Author(s):  
Samiksha Pachade ◽  
Prasanna Porwal ◽  
Manesh Kokare ◽  
Luca Giancardo ◽  
Fabrice Mériaudeau
Keyword(s):  
Optic Disc ◽  
Adversarial Learning

Agreement among ophthalmologists in marking the optic disc and optic cup in fundus images

2016 ◽  
Vol 37 (3) ◽  
pp. 701-717 ◽  
Author(s):  
Ahmed Almazroa ◽  
Sami Alodhayb ◽  
Essameldin Osman ◽  
Eslam Ramadan ◽  
Mohammed Hummadi ◽  
...  
Keyword(s):  
Optic Disc ◽  
Fundus Images ◽  
Optic Cup

Optic disc and optic cup segmentation from retinal images using hybrid approach

2019 ◽  
Vol 127 ◽  
pp. 308-322 ◽  
Author(s):  
Niharika Thakur ◽  
Mamta Juneja
Keyword(s):  
Optic Disc ◽  
Hybrid Approach ◽  
Retinal Images ◽  
Optic Cup

scholarly journals Region Focus Network for Joint Optic Disc and Cup Segmentation

2020 ◽  
Vol 34 (01) ◽  
pp. 751-758
Author(s):  
Ge Li ◽  
Changsheng Li ◽  
Chan Zeng ◽  
Peng Gao ◽  
Guotong Xie
Keyword(s):  
Optic Disc ◽  
Feature Representation ◽  
Fundus Images ◽  
Feature Pooling ◽  
The World ◽  
Optic Cup ◽  
Disc Region ◽  
Class Segmentation ◽  
Fusion Feature ◽  
Screening And Diagnosis

Glaucoma is one of the three leading causes of blindness in the world and is predicted to affect around 80 million people by 2020. The optic cup (OC) to optic disc (OD) ratio (CDR) in fundus images plays a pivotal role in the screening and diagnosis of glaucoma. Existing methods usually crop the optic disc region first, and subsequently perform segmentation in this region. However, these approaches come up with high complexities due to the separate operations. To remedy this issue, we propose a Region Focus Network (RF-Net) that innovatively integrates detection and multi-class segmentation into a unified architecture for end-to-end joint optic disc and cup segmentation with global optimization. The key idea of our method is designing a novel multi-class mask branch which generates a high-quality segmentation in the detected region for both disc and cup. To bridge the connection between the backbone and multi-class mask branch, a Fusion Feature Pooling (FFP) structure is presented to extract features from each level of the pyramid network and fuse them into a final feature representation for segmentation. Extensive experimental results on the REFUGE-2018 challenge dataset and the Drishti-GS dataset show that the proposed method achieves the best performance, compared with competitive approaches reported in the literature and the official leaderboard. Our code will be released soon.

scholarly journals Mixed Maximum Loss Design for Optic Disc and Optic Cup Segmentation with Deep Learning from Imbalanced Samples

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4401 ◽  
Author(s):  
Yong-li Xu ◽  
Shuai Lu ◽  
Han-xiong Li ◽  
Rui-rui Li
Keyword(s):  
Deep Learning ◽  
Optic Disc ◽  
Learning Strategy ◽  
State Of The Art ◽  
Loss Minimization ◽  
Training Strategy ◽  
Multi Scale ◽  
Maximum Loss ◽  
Screening Performance ◽  
Optic Cup

Glaucoma is a serious eye disease that can cause permanent blindness and is difficult to diagnose early. Optic disc (OD) and optic cup (OC) play a pivotal role in the screening of glaucoma. Therefore, accurate segmentation of OD and OC from fundus images is a key task in the automatic screening of glaucoma. In this paper, we designed a U-shaped convolutional neural network with multi-scale input and multi-kernel modules (MSMKU) for OD and OC segmentation. Such a design gives MSMKU a rich receptive field and is able to effectively represent multi-scale features. In addition, we designed a mixed maximum loss minimization learning strategy (MMLM) for training the proposed MSMKU. This training strategy can adaptively sort the samples by the loss function and re-weight the samples through data enhancement, thereby synchronously improving the prediction performance of all samples. Experiments show that the proposed method has obtained a state-of-the-art breakthrough result for OD and OC segmentation on the RIM-ONE-V3 and DRISHTI-GS datasets. At the same time, the proposed method achieved satisfactory glaucoma screening performance on the RIM-ONE-V3 and DRISHTI-GS datasets. On datasets with an imbalanced distribution between typical and rare sample images, the proposed method obtained a higher accuracy than existing deep learning methods.

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