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12 October 2022 Optic disc segmentation in retinal fundus images using improved CE-Net
Yingxue Wang, Lin Huang
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Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 123421F (2022) https://doi.org/10.1117/12.2643259
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China
Abstract
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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Yingxue Wang and Lin Huang "Optic disc segmentation in retinal fundus images using improved CE-Net", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 123421F (12 October 2022); https://doi.org/10.1117/12.2643259
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KEYWORDS
Image segmentation
Network architectures
Convolution
Feature extraction
Computer programming
Medical imaging
Data modeling
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