Kyle V Marra, Jimmy S Chen, Hailey K Robles-Holmes, Kristine B Ly, Joseph Miller, Guoqin Wei, Edith Aguilar, Felicitas Bucher, Yoichi Ideguchi, Fritz Gerald P Kalaw, Andrew C Lin, Napoleone Ferrara, J Peter Campbell, Martin Friedlander, Eric Nudleman
{"title":"缺血性视网膜病变小鼠氧诱导视网膜病变模型平面图像开源数据集的开发。","authors":"Kyle V Marra, Jimmy S Chen, Hailey K Robles-Holmes, Kristine B Ly, Joseph Miller, Guoqin Wei, Edith Aguilar, Felicitas Bucher, Yoichi Ideguchi, Fritz Gerald P Kalaw, Andrew C Lin, Napoleone Ferrara, J Peter Campbell, Martin Friedlander, Eric Nudleman","doi":"10.1167/tvst.13.12.4","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>To describe an open-source dataset of flat-mounted retinal images and vessel segmentations from mice subject to the oxygen-induced retinopathy (OIR) model.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p><p><strong>Translational relevance: </strong>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.</p>","PeriodicalId":23322,"journal":{"name":"Translational Vision Science & Technology","volume":"13 12","pages":"4"},"PeriodicalIF":2.6000,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11620014/pdf/","citationCount":"0","resultStr":"{\"title\":\"Development of an Open-Source Dataset of Flat-Mounted Images for the Murine Oxygen-Induced Retinopathy Model of Ischemic Retinopathy.\",\"authors\":\"Kyle V Marra, Jimmy S Chen, Hailey K Robles-Holmes, Kristine B Ly, Joseph Miller, Guoqin Wei, Edith Aguilar, Felicitas Bucher, Yoichi Ideguchi, Fritz Gerald P Kalaw, Andrew C Lin, Napoleone Ferrara, J Peter Campbell, Martin Friedlander, Eric Nudleman\",\"doi\":\"10.1167/tvst.13.12.4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>To describe an open-source dataset of flat-mounted retinal images and vessel segmentations from mice subject to the oxygen-induced retinopathy (OIR) model.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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. 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Development of an Open-Source Dataset of Flat-Mounted Images for the Murine Oxygen-Induced Retinopathy Model of Ischemic Retinopathy.
Purpose: To describe an open-source dataset of flat-mounted retinal images and vessel segmentations from mice subject to the oxygen-induced retinopathy (OIR) model.
Methods: 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.
Results: 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.
Conclusions: 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.
Translational relevance: 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.
期刊介绍：
Translational Vision Science & Technology (TVST), an official journal of the Association for Research in Vision and Ophthalmology (ARVO), an international organization whose purpose is to advance research worldwide into understanding the visual system and preventing, treating and curing its disorders, is an online, open access, peer-reviewed journal emphasizing multidisciplinary research that bridges the gap between basic research and clinical care. A highly qualified and diverse group of Associate Editors and Editorial Board Members is led by Editor-in-Chief Marco Zarbin, MD, PhD, FARVO.
The journal covers a broad spectrum of work, including but not limited to:
Applications of stem cell technology for regenerative medicine,
Development of new animal models of human diseases,
Tissue bioengineering,
Chemical engineering to improve virus-based gene delivery,
Nanotechnology for drug delivery,
Design and synthesis of artificial extracellular matrices,
Development of a true microsurgical operating environment,
Refining data analysis algorithms to improve in vivo imaging technology,
Results of Phase 1 clinical trials,
Reverse translational ("bedside to bench") research.
TVST seeks manuscripts from scientists and clinicians with diverse backgrounds ranging from basic chemistry to ophthalmic surgery that will advance or change the way we understand and/or treat vision-threatening diseases. TVST encourages the use of color, multimedia, hyperlinks, program code and other digital enhancements.
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