CAFR-CNN: coarse-to-fine adaptive faster R-CNN for cross-domain joint optic disc and cup segmentation

Guo, Yanfei; Peng, Yanjun; Zhang, Bin

doi:10.1007/s10489-020-02145-w

Cited by 11 publications

(2 citation statements)

References 55 publications

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“…Bhatkalkar et al [9] proposed a novel CNN architecture to accurately segment the OD. Guo et al [10] proposed an adaptive framework based on Faster R-CNN. The framework is composed of two networks: coarse network and fine network, which can be used to realize cross-domain OC and OD joint segmentation.…”

Section: Related Workmentioning

confidence: 99%

Joint segmentation of optic cup and optic disc using deep convolutional generative adversarial network

Yu¹

2022

J. Phys.: Conf. Ser.

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Glaucoma, as one of the three major blinding ophthalmic diseases in the world, is usually accompanied by changes in the structure of the patient’s optic disc, such as optic disc atrophy and depression. Clinical ophthalmologists tend to use the cup-disc ratio as an evaluation index to realize the screening and diagnosis of glaucoma. Therefore, the accurate measurement of optic cup (OC), optic disc (OD) and other parameters is of great clinical significance for early screening of glaucoma. Inspired by game theory, this paper combines deep convolutional neural networks (DCNN) with generative adversarial networks (GAN), and proposes a model for the joint segmentation of OC and OD. Specifically, the generator is composed of a deep convolutional encoder-decoder network to jointly segment the OC and OD, and the discriminator is composed of an eight layer full convolutional neural network. The discrimination results adjust the parameters in the structure of the generator and discriminator network through back propagation to achieve the effect of autonomous learning and optimization of the model. When the proposed network and the existing networks are evaluated on the public dataset Drishti-GS1, the research results demonstrate that the proposed network can achieve a significant improvement in the overall performance.

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Section: Related Workmentioning

confidence: 99%

Joint segmentation of optic cup and optic disc using deep convolutional generative adversarial network

Yu¹

2022

J. Phys.: Conf. Ser.

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“…The authors in [ 26 ] proposed a Densenet-77 based Mask R-CNN to address blurred retinal images, reporting an IoU of 0.972. An essential domain-shift problem across different datasets was proposed by Y. Guo et al in [ 27 ], throwing a coarse-to-fine adaptive Faster R-CNN for joint OD and OC segmentation. H. Almubarak et al [ 28 ] made use of a simple two-stage Mask R-CNN, which first detects and cuts around the ONH, then introduces the cropped image with the original one into the new detection network using different scales.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Different Object Detection Models for the Segmentation of Optical Cups and Discs

et al. 2022

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Glaucoma is an eye disease that gradually deteriorates vision. Much research focuses on extracting information from the optic disc and optic cup, the structure used for measuring the cup-to-disc ratio. These structures are commonly segmented with deeplearning techniques, primarily using Encoder–Decoder models, which are hard to train and time-consuming. Object detection models using convolutional neural networks can extract features from fundus retinal images with good precision. However, the superiority of one model over another for a specific task is still being determined. The main goal of our approach is to compare object detection model performance to automate segment cups and discs on fundus images. This study brings the novelty of seeing the behavior of different object detection models in the detection and segmentation of the disc and the optical cup (Mask R-CNN, MS R-CNN, CARAFE, Cascade Mask R-CNN, GCNet, SOLO, Point_Rend), evaluated on Retinal Fundus Images for Glaucoma Analysis (REFUGE), and G1020 datasets. Reported metrics were Average Precision (AP), F1-score, IoU, and AUCPR. Several models achieved the highest AP with a perfect 1.000 when the threshold for IoU was set up at 0.50 on REFUGE, and the lowest was Cascade Mask R-CNN with an AP of 0.997. On the G1020 dataset, the best model was Point_Rend with an AP of 0.956, and the worst was SOLO with 0.906. It was concluded that the methods reviewed achieved excellent performance with high precision and recall values, showing efficiency and effectiveness. The problem of how many images are needed was addressed with an initial value of 100, with excellent results. Data augmentation, multi-scale handling, and anchor box size brought improvements. The capability to translate knowledge from one database to another shows promising results too.

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IrisST-Net for iris segmentation and contour parameters extraction

Lei

Shen

et al. 2022

Appl Intell

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CAFR-CNN: coarse-to-fine adaptive faster R-CNN for cross-domain joint optic disc and cup segmentation

Cited by 11 publications

References 55 publications

Joint segmentation of optic cup and optic disc using deep convolutional generative adversarial network

Joint segmentation of optic cup and optic disc using deep convolutional generative adversarial network

Performance Evaluation of Different Object Detection Models for the Segmentation of Optical Cups and Discs

IrisST-Net for iris segmentation and contour parameters extraction

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