Open ultrawidefield fundus image dataset with disease diagnosis and clinical image quality assessment
Abstract Ultrawidefield fundus (UWF) images have a wide imaging range (200° of the retinal region), which offers the opportunity to show more information for ophthalmic diseases. Image quality assessment (IQA) is a prerequisite for applying UWF and is crucial for developing artificial intelligence-d...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-024-04113-2 |
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| author | Shucheng He Xin Ye Wenbin Xie Yingjiao Shen Shangchao Yang Xiaxing Zhong Hanyi Guan Xiangpeng Zhou Jiang Wu Lijun Shen |
| author_facet | Shucheng He Xin Ye Wenbin Xie Yingjiao Shen Shangchao Yang Xiaxing Zhong Hanyi Guan Xiangpeng Zhou Jiang Wu Lijun Shen |
| author_sort | Shucheng He |
| collection | DOAJ |
| description | Abstract Ultrawidefield fundus (UWF) images have a wide imaging range (200° of the retinal region), which offers the opportunity to show more information for ophthalmic diseases. Image quality assessment (IQA) is a prerequisite for applying UWF and is crucial for developing artificial intelligence-driven diagnosis and screening systems. Most image quality systems have been applied to the assessments of natural images, but whether these systems are suitable for evaluating the UWF image quality remains debatable. Additionally, existing IQA datasets only provide photographs of diabetic retinopathy (DR) patients and quality evaluation results applicable for natural image, neglecting patients’ clinical information. To address these issues, we established a real-world clinical practice ultra-widefield fundus images dataset, with 700 high-resolution UWF images and corresponding clinical information from six common fundus diseases and healthy volunteers. The image quality is annotated by three ophthalmologists based on the field of view, illumination, artifact, contrast, and overall quality. This dataset illustrates the distribution of UWF image quality across diseases in clinical practice, offering a foundation for developing effective IQA systems. |
| format | Article |
| id | doaj-art-33e4b898b72b4bbf968c2f123789c939 |
| institution | OA Journals |
| issn | 2052-4463 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-33e4b898b72b4bbf968c2f123789c9392025-08-20T02:22:25ZengNature PortfolioScientific Data2052-44632024-11-011111710.1038/s41597-024-04113-2Open ultrawidefield fundus image dataset with disease diagnosis and clinical image quality assessmentShucheng He0Xin Ye1Wenbin Xie2Yingjiao Shen3Shangchao Yang4Xiaxing Zhong5Hanyi Guan6Xiangpeng Zhou7Jiang Wu8Lijun Shen9Department of Ophthalmology, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College)Zhejiang Provincial People’s Hospital Bijie HospitalZhejiang Provincial People’s Hospital Bijie HospitalDepartment of Ophthalmology, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College)Wenzhou Medical UniversityWenzhou Medical UniversityWenzhou Medical UniversityWenzhou Medical UniversityHangzhou Medical CollegeDepartment of Ophthalmology, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College)Abstract Ultrawidefield fundus (UWF) images have a wide imaging range (200° of the retinal region), which offers the opportunity to show more information for ophthalmic diseases. Image quality assessment (IQA) is a prerequisite for applying UWF and is crucial for developing artificial intelligence-driven diagnosis and screening systems. Most image quality systems have been applied to the assessments of natural images, but whether these systems are suitable for evaluating the UWF image quality remains debatable. Additionally, existing IQA datasets only provide photographs of diabetic retinopathy (DR) patients and quality evaluation results applicable for natural image, neglecting patients’ clinical information. To address these issues, we established a real-world clinical practice ultra-widefield fundus images dataset, with 700 high-resolution UWF images and corresponding clinical information from six common fundus diseases and healthy volunteers. The image quality is annotated by three ophthalmologists based on the field of view, illumination, artifact, contrast, and overall quality. This dataset illustrates the distribution of UWF image quality across diseases in clinical practice, offering a foundation for developing effective IQA systems.https://doi.org/10.1038/s41597-024-04113-2 |
| spellingShingle | Shucheng He Xin Ye Wenbin Xie Yingjiao Shen Shangchao Yang Xiaxing Zhong Hanyi Guan Xiangpeng Zhou Jiang Wu Lijun Shen Open ultrawidefield fundus image dataset with disease diagnosis and clinical image quality assessment Scientific Data |
| title | Open ultrawidefield fundus image dataset with disease diagnosis and clinical image quality assessment |
| title_full | Open ultrawidefield fundus image dataset with disease diagnosis and clinical image quality assessment |
| title_fullStr | Open ultrawidefield fundus image dataset with disease diagnosis and clinical image quality assessment |
| title_full_unstemmed | Open ultrawidefield fundus image dataset with disease diagnosis and clinical image quality assessment |
| title_short | Open ultrawidefield fundus image dataset with disease diagnosis and clinical image quality assessment |
| title_sort | open ultrawidefield fundus image dataset with disease diagnosis and clinical image quality assessment |
| url | https://doi.org/10.1038/s41597-024-04113-2 |
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