TY - JOUR
T1 - Chákṣu
T2 - A glaucoma specific fundus image database
AU - Kumar, J. R.Harish
AU - Seelamantula, Chandra Sekhar
AU - Gagan, J. H.
AU - Kamath, Yogish S.
AU - Kuzhuppilly, Neetha I.R.
AU - Vivekanand, U.
AU - Gupta, Preeti
AU - Patil, Shilpa
N1 - Funding Information:
This research was supported by the IMPacting Research INnovation and Technology (IMPRINT) - India (Project ID: 6013), a flagship national initiative by the Ministry of Human Resource and Development (MHRD), Government of India and Science and Engineering Research Board (SERB) - Teachers Associateship for Research Excellence (TARE) Fellowship (Project id: TAR/2019/000037). We would like to thank Manipal Academy of Higher Education (MAHE), Manipal for permitting us to acquire retinal fundus images at its constituent institutions. Thanks to all the subjects for consenting and taking part in the fundus examination. We would like to thank Remidio Innovative Solutions Pvt. Ltd., Forus Health Pvt. Ltd., and Bosch Eye Care Solutions, Bengaluru, India, for the support provided during fundus image acquisition. We would also like to thank Subramanya Jois, Harsha Sridhar, Harshit Shirsat, and Aniketh Manjunath for insightful technical discussions and also for being part of the data acquisition and curation.
Funding Information:
This research was supported by the IMPacting Research INnovation and Technology (IMPRINT) - India (Project ID: 6013), a flagship national initiative by the Ministry of Human Resource and Development (MHRD), Government of India and Science and Engineering Research Board (SERB) - Teachers Associateship for Research Excellence (TARE) Fellowship (Project id: TAR/2019/000037). We would like to thank Manipal Academy of Higher Education (MAHE), Manipal for permitting us to acquire retinal fundus images at its constituent institutions. Thanks to all the subjects for consenting and taking part in the fundus examination. We would like to thank Remidio Innovative Solutions Pvt. Ltd., Forus Health Pvt. Ltd., and Bosch Eye Care Solutions, Bengaluru, India, for the support provided during fundus image acquisition. We would also like to thank Subramanya Jois, Harsha Sridhar, Harshit Shirsat, and Aniketh Manjunath for insightful technical discussions and also for being part of the data acquisition and curation.
Publisher Copyright:
© 2023, The Author(s).
PY - 2023/12
Y1 - 2023/12
N2 - We introduce Chákṣu–a retinal fundus image database for the evaluation of computer-assisted glaucoma prescreening techniques. The database contains 1345 color fundus images acquired using three brands of commercially available fundus cameras. Each image is provided with the outlines for the optic disc (OD) and optic cup (OC) using smooth closed contours and a decision of normal versus glaucomatous by five expert ophthalmologists. In addition, segmentation ground-truths of the OD and OC are provided by fusing the expert annotations using the mean, median, majority, and Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm. The performance indices show that the ground-truth agreement with the experts is the best with STAPLE algorithm, followed by majority, median, and mean. The vertical, horizontal, and area cup-to-disc ratios are provided based on the expert annotations. Image-wise glaucoma decisions are also provided based on majority voting among the experts. Chákṣu is the largest Indian-ethnicity-specific fundus image database with expert annotations and would aid in the development of artificial intelligence based glaucoma diagnostics.
AB - We introduce Chákṣu–a retinal fundus image database for the evaluation of computer-assisted glaucoma prescreening techniques. The database contains 1345 color fundus images acquired using three brands of commercially available fundus cameras. Each image is provided with the outlines for the optic disc (OD) and optic cup (OC) using smooth closed contours and a decision of normal versus glaucomatous by five expert ophthalmologists. In addition, segmentation ground-truths of the OD and OC are provided by fusing the expert annotations using the mean, median, majority, and Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm. The performance indices show that the ground-truth agreement with the experts is the best with STAPLE algorithm, followed by majority, median, and mean. The vertical, horizontal, and area cup-to-disc ratios are provided based on the expert annotations. Image-wise glaucoma decisions are also provided based on majority voting among the experts. Chákṣu is the largest Indian-ethnicity-specific fundus image database with expert annotations and would aid in the development of artificial intelligence based glaucoma diagnostics.
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U2 - 10.1038/s41597-023-01943-4
DO - 10.1038/s41597-023-01943-4
M3 - Article
C2 - 36737439
AN - SCOPUS:85147423187
SN - 2052-4463
VL - 10
JO - Scientific data
JF - Scientific data
IS - 1
M1 - 70
ER -