Marra Kyle V, Chen Jimmy S, Robles-Holmes Hailey K, Ly Kristine B, Miller Joseph, Wei Guoqin, Aguilar Edith, Bucher Felicitas, Ideguchi Yoichi, Kalaw Fritz Gerald P, Lin Andrew C, Ferrara Napoleone, Campbell J Peter, Friedlander Martin, Nudleman Eric
Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California San Diego, San Diego, CA, USA.
Molecular Medicine, The Scripps Research Institute, San Diego, CA, USA.
Transl Vis Sci Technol. 2024 Dec 2;13(12):4. doi: 10.1167/tvst.13.12.4.
To describe an open-source dataset of flat-mounted retinal images and vessel segmentations from mice subject to the oxygen-induced retinopathy (OIR) model.
Flat-mounted retinal images from mice killed at postnatal days 12 (P12), P17, and P25 used in prior OIR studies were compiled. Mice subjected to normoxic conditions were killed at P12, P17, and P25, and their retinas were flat-mounted for imaging. Major blood vessels from the OIR images were manually segmented by four graders (JSC, HKR, KBL, JM), with cross-validation performed to ensure similar grading.
Overall, 1170 images were included in this dataset. Of these images, 111 were of normoxic mice retina, and 1048 were mice subject to OIR. The majority of images from OIR mice were obtained at P17. The 50 images obtained from an external dataset, OIRSeg, did not have age labels. All images were manually segmented and used in the training or testing of a previously published deep learning algorithm.
This is the first open-source dataset of original and segmented flat-mounted retinal images. The dataset has potential applications for expanding the development of generalizable and larger-scale artificial intelligence and analyses for OIR. This dataset is published online and publicly available at dx.doi.org/10.6084/m9.figshare.23690973.
This open access dataset serves as a source of raw data for future research involving big data and artificial intelligence research concerning oxygen-induced retinopathy.
描述一个来自氧诱导视网膜病变（OIR）模型小鼠的平铺视网膜图像和血管分割的开源数据集。
收集先前OIR研究中在出生后第12天（P12）、P17和P25处死的小鼠的平铺视网膜图像。将处于常氧条件下的小鼠在P12、P17和P25处死，并将其视网膜平铺用于成像。由四名评分者（JSC、HKR、KBL、JM）手动分割OIR图像中的主要血管，并进行交叉验证以确保分级相似。
该数据集总共包含1170张图像。其中，111张是常氧小鼠视网膜图像，1048张是OIR小鼠的图像。来自OIR小鼠的大多数图像是在P17获得的。从外部数据集OIRSeg获得的50张图像没有年龄标签。所有图像均经过手动分割，并用于先前发表的深度学习算法的训练或测试。
这是第一个原始的和平铺视网膜图像分割的开源数据集。该数据集在扩展可推广的大规模人工智能开发以及OIR分析方面具有潜在应用。该数据集已在线发表，可在dx.doi.org/10.6084/m9.figshare.23690973上公开获取。
这个开放获取的数据集为未来涉及大数据和氧诱导视网膜病变人工智能研究的研究提供了原始数据来源。
