Dataset for identifying paracentral acute middle maculopathy lesions in spectral-domain optical coherence tomography imagesMendeley Data
This paper introduces a comprehensive dataset of spectral-domain optical coherence tomography (SD-OCT) images of human eyes affected by paracentral acute middle maculopathy (PAMM). Acquired with an SD-OCT device (Optovue, Fremont, California, USA), the dataset includes 133 OCT images of lesions. Eac...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340924010837 |
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| author | Tianqiao Zhang Mengjiao Zhang Dexun Zhang Wenjing Meng Zhenzhen Li Zhengwei Zhang |
| author_facet | Tianqiao Zhang Mengjiao Zhang Dexun Zhang Wenjing Meng Zhenzhen Li Zhengwei Zhang |
| author_sort | Tianqiao Zhang |
| collection | DOAJ |
| description | This paper introduces a comprehensive dataset of spectral-domain optical coherence tomography (SD-OCT) images of human eyes affected by paracentral acute middle maculopathy (PAMM). Acquired with an SD-OCT device (Optovue, Fremont, California, USA), the dataset includes 133 OCT images of lesions. Each image is paired with a corresponding YOLO label in TXT format, representing manually annotated lesion regions of PAMM, created with the assistance of ophthalmologists. This dataset is invaluable for developing and evaluating automatic algorithms for diagnosing PAMM lesions. By providing detailed annotations and high-quality images, it facilitates advancements in understanding the morphology, progression, and potential treatments of PAMM. Furthermore, it supports the improvement of diagnostic accuracy and the development of targeted therapeutic interventions for retinal diseases. This resource addresses a significant gap in the availability of public datasets focused on PAMM lesions, promoting further research in automated intelligent analysis systems for retinal OCT images. |
| format | Article |
| id | doaj-art-0767aa02e5a441d4b1f2e9b47be1e50a |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-0767aa02e5a441d4b1f2e9b47be1e50a2024-11-30T07:11:10ZengElsevierData in Brief2352-34092024-12-0157111121Dataset for identifying paracentral acute middle maculopathy lesions in spectral-domain optical coherence tomography imagesMendeley DataTianqiao Zhang0Mengjiao Zhang1Dexun Zhang2Wenjing Meng3Zhenzhen Li4Zhengwei Zhang5School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China; Guangxi Human Physiological Information Non Invasive Detection Engineering Technology Research Center, Guilin, China; Guangxi Colleges and Universities Key Laboratory of Biomedical Sensors and Intelligent Instruments, Guilin, ChinaSchool of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, ChinaSchool of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, ChinaDepartment of Library Services, Guilin University of Electronic Technology, Guilin, ChinaSchool of Information Engineering, Nanchang Institute of Technology, Nanchang, China; Corresponding author.Department of Ophthalmology, Jiangnan University Medical Center, Wuxi, China; Department of Ophthalmology, Wuxi No.2 People's Hospital, Affiliated Wuxi Clinical College of Nantong University, Wuxi, China; Corresponding author at: Department of Ophthalmology, Jiangnan University Medical Center, Wuxi, China.This paper introduces a comprehensive dataset of spectral-domain optical coherence tomography (SD-OCT) images of human eyes affected by paracentral acute middle maculopathy (PAMM). Acquired with an SD-OCT device (Optovue, Fremont, California, USA), the dataset includes 133 OCT images of lesions. Each image is paired with a corresponding YOLO label in TXT format, representing manually annotated lesion regions of PAMM, created with the assistance of ophthalmologists. This dataset is invaluable for developing and evaluating automatic algorithms for diagnosing PAMM lesions. By providing detailed annotations and high-quality images, it facilitates advancements in understanding the morphology, progression, and potential treatments of PAMM. Furthermore, it supports the improvement of diagnostic accuracy and the development of targeted therapeutic interventions for retinal diseases. This resource addresses a significant gap in the availability of public datasets focused on PAMM lesions, promoting further research in automated intelligent analysis systems for retinal OCT images.http://www.sciencedirect.com/science/article/pii/S2352340924010837Bounding box annotationOphthalmologyRetinal diseaseAnomaly detection |
| spellingShingle | Tianqiao Zhang Mengjiao Zhang Dexun Zhang Wenjing Meng Zhenzhen Li Zhengwei Zhang Dataset for identifying paracentral acute middle maculopathy lesions in spectral-domain optical coherence tomography imagesMendeley Data Data in Brief Bounding box annotation Ophthalmology Retinal disease Anomaly detection |
| title | Dataset for identifying paracentral acute middle maculopathy lesions in spectral-domain optical coherence tomography imagesMendeley Data |
| title_full | Dataset for identifying paracentral acute middle maculopathy lesions in spectral-domain optical coherence tomography imagesMendeley Data |
| title_fullStr | Dataset for identifying paracentral acute middle maculopathy lesions in spectral-domain optical coherence tomography imagesMendeley Data |
| title_full_unstemmed | Dataset for identifying paracentral acute middle maculopathy lesions in spectral-domain optical coherence tomography imagesMendeley Data |
| title_short | Dataset for identifying paracentral acute middle maculopathy lesions in spectral-domain optical coherence tomography imagesMendeley Data |
| title_sort | dataset for identifying paracentral acute middle maculopathy lesions in spectral domain optical coherence tomography imagesmendeley data |
| topic | Bounding box annotation Ophthalmology Retinal disease Anomaly detection |
| url | http://www.sciencedirect.com/science/article/pii/S2352340924010837 |
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