Dataset for identifying paracentral acute middle maculopathy lesions in spectral-domain optical coherence tomography imagesMendeley Data
Tianqiao Zhang,
Mengjiao Zhang,
Dexun Zhang,
Wenjing Meng,
Zhenzhen Li,
Zhengwei Zhang
Affiliations
Tianqiao Zhang
School 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, China
Mengjiao Zhang
School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China
Dexun Zhang
School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China
Wenjing Meng
Department of Library Services, Guilin University of Electronic Technology, Guilin, China
Zhenzhen Li
School of Information Engineering, Nanchang Institute of Technology, Nanchang, China; Corresponding author.
Zhengwei Zhang
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.
