An Efficient Weakly-Supervised Learning Method for Optic Disc Segmentation

Wen, Yang; Chen, Leiting; Qiao, Liang; Zhou, Chuan; Xi, Shuo; Guo, Rui; Deng, Yu

doi:10.1109/bibm49941.2020.9313558

Cited by 5 publications

(3 citation statements)

References 38 publications

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“…We compared the performance results obtained in a previous study [24] and the performance results of the latest CNN-based segmentation models (U-Net [23], CE-Net [25], HyNet [26] and DeepLabv3+ [15]) with those of our method.…”

Section: Comparison With Other Segmentation Methodsmentioning

confidence: 99%

Let’s Find Fluorescein: Cross-Modal Dual Attention Learning For Fluorescein Leakage Segmentation In Fundus Fluorescein Angiography

Yang¹,

Chen²,

Qiao

et al. 2021

2021 IEEE International Conference on Multimedia and Expo (ICME)

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Automatic segmentation of fluorescein leakage in fundus fluorescein angiography images is important in the clinical diagnosis of advanced diabetic retinopathy. Despite the recent success of deep-learning-based models in improving medical image segmentation, segmentation of fluorescein leakage has been ignored owing to (1) a lack of publicly available data with sufficient annotations for training a segmentation network and (2) incapability of supervised models to accurately localize fluorescein leakage at different imaging angles. To address these issues, we studied the automatic segmentation of fluorescein leakage in fundus fluorescein angiography images and devised a method involving (1) a cross-modal learning framework for fluorescein leakage segmentation using both image and text data, (2) a dual attention learning module for identifying important linguistic and visual features, and (3) fluorescein-related-keyword classification for identifying meaningful textual expressions pertaining to the location and type of fluorescein leakage. We demonstrate the effectiveness of the proposed method for an in-house fundus fluorescein angiography image data set.

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Section: Comparison With Other Segmentation Methodsmentioning

confidence: 99%

Let’s Find Fluorescein: Cross-Modal Dual Attention Learning For Fluorescein Leakage Segmentation In Fundus Fluorescein Angiography

Yang¹,

Chen²,

Qiao

et al. 2021

2021 IEEE International Conference on Multimedia and Expo (ICME)

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“…In transfer learning, the pretrained neural architectures such as visual geometric group, residual network and GoogleNet are employed. Super pixel segmentation approach is discussed in [19] for glaucoma diagnosis. CDR is the main parameter used for glaucoma diagnosis after segmentation of OC and OD regions by super-pixel classification.…”

Section: Introductionmentioning

confidence: 99%

Soft Computing Based Discriminator Model for Glaucoma Diagnosis

Rebinth¹,

Kumar²

2022

Computer Systems Science and Engineering

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In this study, a Discriminator Model for Glaucoma Diagnosis (DMGD) using soft computing techniques is presented. As the biomedical images such as fundus images are often acquired in high resolution, the Region of Interest (ROI) for glaucoma diagnosis must be selected at first to reduce the complexity of any system. The DMGD system uses a series of pre-processing; initial cropping by the green channel's intensity, Spatially Weighted Fuzzy C Means (SWFCM), blood vessel detection and removal by Gaussian Derivative Filters (GDF) and inpainting algorithms. Once the ROI has been selected, the numerical features such as colour, spatial domain features from Local Binary Pattern (LBP) and frequency domain features from LAWS are generated from the corresponding ROI for further classification using kernel based Support Vector Machine (SVM). The DMGD system performances are validated using four fundus image databases; ORIGA, RIM-ONE, DRISHTI-GS1, and HRF with four different kernels; Linear Kernel (LK), Polynomial Kernel (PK), Radial Basis Function (RBFK) kernel, Quadratic Kernel (QK) based SVM classifiers. Results show that the DMGD system classifies the fundus images accurately using the multiple features and kernel based classifies from the properly segmented ROI.

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“…Most methods for optic disc segmentation have achieved great results. Wen et al 9 proposed an improved U-shaped CNN with cross-scale connections, namely Hybrid Network (Hy Net) and Hybrid Process (Hy Process) training methods. Hy Process scheme trains Hy Net to segment optic disc through weak supervision and complete supervision.…”

Section: Introductionmentioning

confidence: 99%

Optic disc segmentation in retinal fundus images using improved CE-Net

wang

Huang²

2022

Fourteenth International Conference on Digital Image Processing (ICDIP 2022)

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Diabetic retinopathy is one of the main complications of diabetes and the most important factor leading to blindness in the late stage of the disease. It often manifests as one or more lesions in clinical diagnosis. In order to reduce the difficulty of detection, it is of great significance to segment the optic disc in retinal images. This paper proposes an improved context encoding network architecture (CE-Net) for segmentation of the optic disc portion in diabetic retinal images. The network architecture is divided into three parts: feature encoder module, context extractor module and feature decoder module. The context extractor module consists of an improved dense atrous convolutional block (DAC) and residual multi-kernel pooling (RMP). Experimental result shows that the optimal network model generated by the improved CE-Net architecture has good performance on the Indian Diabetic Retinopathy Image Dataset (IDRID), and compared with other methods, our method has the lowest mean overlap error and the highest accuracy and sensitivity.

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An Efficient Weakly-Supervised Learning Method for Optic Disc Segmentation

Cited by 5 publications

References 38 publications

Let’s Find Fluorescein: Cross-Modal Dual Attention Learning For Fluorescein Leakage Segmentation In Fundus Fluorescein Angiography

Let’s Find Fluorescein: Cross-Modal Dual Attention Learning For Fluorescein Leakage Segmentation In Fundus Fluorescein Angiography

Soft Computing Based Discriminator Model for Glaucoma Diagnosis

Optic disc segmentation in retinal fundus images using improved CE-Net

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