Comparison of Fully Convolutional Networks and U-Net for Optic Disc and Optic Cup Segmentation
Glaucoma, the leading cause of irreversible blindness, must be diagnosed early and thus treated in time. However, it has no noticeable symptoms in its early stages and may not be detected easily. This paper aims to compare two well-known convolutional neural network (CNN) structures, namely Fully Co...
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_03022.pdf |
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| author | Jin Zixiao |
| author_facet | Jin Zixiao |
| author_sort | Jin Zixiao |
| collection | DOAJ |
| description | Glaucoma, the leading cause of irreversible blindness, must be diagnosed early and thus treated in time. However, it has no noticeable symptoms in its early stages and may not be detected easily. This paper aims to compare two well-known convolutional neural network (CNN) structures, namely Fully Convolutional Networks (FCNs) and U-Net for the segmentation of the optic disc (OD) and optic cup (OC) from retinal fundus images which play an important role in glaucoma diagnosis. The performance of both models is assessed using qualitative parameters such as the Dice coefficient, Jaccard index, and cup-to-disc ratio (CDR) error. In our experiment, the U-Net model yields more accurate segmentation results with 0.9601 average pixel accuracy and 0.9255 dice score for OD segmentation, outperforming the FCNs model with 0.9560 average pixel accuracy and 0.9132 dice score for OD segmentation. However, FCNs have a shorter inference time of 0. 0043 seconds against U-net’s 0. 0062 seconds making FCNs more suitable for real-time applications. The restrictions related to this study include biases from using only one dataset acquired from particular imaging devices, dependency on mask-based cropping techniques, and comparison being restricted to two fundamental architectures. This work presents the contribution of the deep learning models in improving glaucoma screening and therefore helping in avoiding blindness. |
| format | Article |
| id | doaj-art-ce70b50d6f2e4ad388d77197fc169f9d |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-ce70b50d6f2e4ad388d77197fc169f9d2025-02-07T08:21:11ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700302210.1051/itmconf/20257003022itmconf_dai2024_03022Comparison of Fully Convolutional Networks and U-Net for Optic Disc and Optic Cup SegmentationJin Zixiao0College of Engineering, University of CaliforniaGlaucoma, the leading cause of irreversible blindness, must be diagnosed early and thus treated in time. However, it has no noticeable symptoms in its early stages and may not be detected easily. This paper aims to compare two well-known convolutional neural network (CNN) structures, namely Fully Convolutional Networks (FCNs) and U-Net for the segmentation of the optic disc (OD) and optic cup (OC) from retinal fundus images which play an important role in glaucoma diagnosis. The performance of both models is assessed using qualitative parameters such as the Dice coefficient, Jaccard index, and cup-to-disc ratio (CDR) error. In our experiment, the U-Net model yields more accurate segmentation results with 0.9601 average pixel accuracy and 0.9255 dice score for OD segmentation, outperforming the FCNs model with 0.9560 average pixel accuracy and 0.9132 dice score for OD segmentation. However, FCNs have a shorter inference time of 0. 0043 seconds against U-net’s 0. 0062 seconds making FCNs more suitable for real-time applications. The restrictions related to this study include biases from using only one dataset acquired from particular imaging devices, dependency on mask-based cropping techniques, and comparison being restricted to two fundamental architectures. This work presents the contribution of the deep learning models in improving glaucoma screening and therefore helping in avoiding blindness.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_03022.pdf |
| spellingShingle | Jin Zixiao Comparison of Fully Convolutional Networks and U-Net for Optic Disc and Optic Cup Segmentation ITM Web of Conferences |
| title | Comparison of Fully Convolutional Networks and U-Net for Optic Disc and Optic Cup Segmentation |
| title_full | Comparison of Fully Convolutional Networks and U-Net for Optic Disc and Optic Cup Segmentation |
| title_fullStr | Comparison of Fully Convolutional Networks and U-Net for Optic Disc and Optic Cup Segmentation |
| title_full_unstemmed | Comparison of Fully Convolutional Networks and U-Net for Optic Disc and Optic Cup Segmentation |
| title_short | Comparison of Fully Convolutional Networks and U-Net for Optic Disc and Optic Cup Segmentation |
| title_sort | comparison of fully convolutional networks and u net for optic disc and optic cup segmentation |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_03022.pdf |
| work_keys_str_mv | AT jinzixiao comparisonoffullyconvolutionalnetworksandunetforopticdiscandopticcupsegmentation |