RIM-ONE DL: A Unified Retinal Image Database for Assessing Glaucoma Using Deep Learning
The first version of the Retinal IMage database for Optic Nerve Evaluation (RIM-ONE) was published in 2011. This was followed by two more, turning it into one of the most cited public retinography databases for evaluating glaucoma. Although it was initially intended to be a database with reference i...
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| Format: | Article |
| Language: | English |
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Slovenian Society for Stereology and Quantitative Image Analysis
2020-11-01
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| Series: | Image Analysis and Stereology |
| Subjects: | |
| Online Access: | https://www.ias-iss.org/ojs/IAS/article/view/2346 |
| _version_ | 1818414384773332992 |
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| author | Francisco José Fumero Batista Tinguaro Diaz-Aleman Jose Sigut Silvia Alayon Rafael Arnay Denisse Angel-Pereira |
| author_facet | Francisco José Fumero Batista Tinguaro Diaz-Aleman Jose Sigut Silvia Alayon Rafael Arnay Denisse Angel-Pereira |
| author_sort | Francisco José Fumero Batista |
| collection | DOAJ |
| description | The first version of the Retinal IMage database for Optic Nerve Evaluation (RIM-ONE) was published in 2011. This was followed by two more, turning it into one of the most cited public retinography databases for evaluating glaucoma. Although it was initially intended to be a database with reference images for segmenting the optic disc, in recent years we have observed that its use has been more oriented toward training and testing deep learning models. The recent REFUGE challenge laid out some criteria that a set of images of these characteristics must satisfy to be used as a standard reference for validating deep learning methods that rely on the use of these data. This, combined with the certain confusion and even improper use observed in some cases of the three versions published, led us to consider revising and combining them into a new, publicly available version called RIM-ONE DL (RIM-ONE for Deep Learning). This paper describes this set of images, consisting of 313 retinographies from normal subjects and 172 retinographies from patients with glaucoma. All of these images have been assessed by two experts and include a manual segmentation of the disc and cup. It also describes an evaluation benchmark with different models of well-known convolutional neural networks. |
| first_indexed | 2024-12-14T11:18:15Z |
| format | Article |
| id | doaj.art-88b5d524607c4858902ee75c618a16ae |
| institution | Directory Open Access Journal |
| issn | 1580-3139 1854-5165 |
| language | English |
| last_indexed | 2024-12-14T11:18:15Z |
| publishDate | 2020-11-01 |
| publisher | Slovenian Society for Stereology and Quantitative Image Analysis |
| record_format | Article |
| series | Image Analysis and Stereology |
| spelling | doaj.art-88b5d524607c4858902ee75c618a16ae2022-12-21T23:03:55ZengSlovenian Society for Stereology and Quantitative Image AnalysisImage Analysis and Stereology1580-31391854-51652020-11-0139316116710.5566/ias.23461049RIM-ONE DL: A Unified Retinal Image Database for Assessing Glaucoma Using Deep LearningFrancisco José Fumero Batista0Tinguaro Diaz-Aleman1Jose Sigut2Silvia Alayon3Rafael Arnay4Denisse Angel-Pereira5Department of Computer Engineering and Systems, University of La LagunaServicio de Oftalmología, Hospital Universitario de CanariasDepartment of Computer Engineering and Systems, University of La LagunaDepartment of Computer Engineering and Systems, University of La LagunaDepartment of Computer Engineering and Systems, University of La LagunaServicio de Oftalmología, Hospital Universitario de CanariasThe first version of the Retinal IMage database for Optic Nerve Evaluation (RIM-ONE) was published in 2011. This was followed by two more, turning it into one of the most cited public retinography databases for evaluating glaucoma. Although it was initially intended to be a database with reference images for segmenting the optic disc, in recent years we have observed that its use has been more oriented toward training and testing deep learning models. The recent REFUGE challenge laid out some criteria that a set of images of these characteristics must satisfy to be used as a standard reference for validating deep learning methods that rely on the use of these data. This, combined with the certain confusion and even improper use observed in some cases of the three versions published, led us to consider revising and combining them into a new, publicly available version called RIM-ONE DL (RIM-ONE for Deep Learning). This paper describes this set of images, consisting of 313 retinographies from normal subjects and 172 retinographies from patients with glaucoma. All of these images have been assessed by two experts and include a manual segmentation of the disc and cup. It also describes an evaluation benchmark with different models of well-known convolutional neural networks.https://www.ias-iss.org/ojs/IAS/article/view/2346convolutional neural networksdeep learningglaucoma assessmentrim-one |
| spellingShingle | Francisco José Fumero Batista Tinguaro Diaz-Aleman Jose Sigut Silvia Alayon Rafael Arnay Denisse Angel-Pereira RIM-ONE DL: A Unified Retinal Image Database for Assessing Glaucoma Using Deep Learning Image Analysis and Stereology convolutional neural networks deep learning glaucoma assessment rim-one |
| title | RIM-ONE DL: A Unified Retinal Image Database for Assessing Glaucoma Using Deep Learning |
| title_full | RIM-ONE DL: A Unified Retinal Image Database for Assessing Glaucoma Using Deep Learning |
| title_fullStr | RIM-ONE DL: A Unified Retinal Image Database for Assessing Glaucoma Using Deep Learning |
| title_full_unstemmed | RIM-ONE DL: A Unified Retinal Image Database for Assessing Glaucoma Using Deep Learning |
| title_short | RIM-ONE DL: A Unified Retinal Image Database for Assessing Glaucoma Using Deep Learning |
| title_sort | rim one dl a unified retinal image database for assessing glaucoma using deep learning |
| topic | convolutional neural networks deep learning glaucoma assessment rim-one |
| url | https://www.ias-iss.org/ojs/IAS/article/view/2346 |
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