The U-NET Via Batch Norm Model for Optic Disc Extraction and Segmentation in Retinal Image
Authors: 
Nan Chen, Yi Zhao, Jun Li, Danni Yang, Shucheng Zhou, Lanyan XueAuthors Info & Claims
Nan Chen, Yi Zhao, Jun Li, Danni Yang, Shucheng Zhou, Lanyan XueAuthors Info & ClaimsPages 511 - 514
Abstract
The segmentation and location of the optic disc in retinal images is of great significance for early diagnosis of glaucoma. To solve the issue, a novel optic disc segmentation and location algorithm is proposed by U-NET network combing the BatchNorm structure. And the morphological opening and reconstruction are used to highlight the position of optic disc and the improved U-NET is utilized to train the segmentation model. The public database IDRiD is used to evaluate the performance of the proposed algorithm. Experimental results indicate that the U-NET can obtain better optic disc structure, especially for the extraction of optic disc edge. The average accuracy is 0.9972, sensitivity is 0.9835, specificity is 0.9975, and Area Under Curve is up to 0.9458, Dice is 0.9435, mIoU is 0.8932. The performances are more competitive than state-of-the-art methods.
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- The U-NET Via Batch Norm Model for Optic Disc Extraction and Segmentation in Retinal Image
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Published In
March 2022
809 pages
ISBN:9781450396110
DOI:10.1145/3532213
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Association for Computing Machinery
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Published: 13 July 2022
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ICCAI '22
ICCAI '22: 2022 8th International Conference on Computing and Artificial Intelligence
March 18 - 21, 2022
Tianjin, China
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