Bhavna J Antony1, Woojin Jeong2, Michael D Abràmoff3, Joseph Vance4, Elliott H Sohn5, Mona K Garvin1. 1. Department of Electrical & Computer Engineering, The University of Iowa, Iowa City, IA, USA ; VA Center for the Prevention and Treatment of Visual Loss, Iowa City, IA, USA. 2. Department of Ophthalmology, Dong-A University, College of Medicine and Medical Research Center, Busan, Korea ; Department of Ophthalmology & Visual Science, The University of Iowa Hospitals and Clinics, Iowa City, IA, USA. 3. Department of Electrical & Computer Engineering, The University of Iowa, Iowa City, IA, USA ; VA Center for the Prevention and Treatment of Visual Loss, Iowa City, IA, USA ; Department of Ophthalmology & Visual Science, The University of Iowa Hospitals and Clinics, Iowa City, IA, USA ; Department of Biomedical Engineering, The University of Iowa, Iowa City, IA, USA. 4. Bioptigen Inc., Morrisville, NC, USA. 5. Department of Ophthalmology & Visual Science, The University of Iowa Hospitals and Clinics, Iowa City, IA, USA.
Abstract
PURPOSE: To describe an adaptation of an existing graph-theoretic method (initially developed for human optical coherence tomography [OCT] images) for the three-dimensional (3D) automated segmentation of 10 intraretinal surfaces in mice scans, and assess the accuracy of the method and the reproducibility of thickness measurements. METHODS: Ten intraretinal surfaces were segmented in repeat spectral domain (SD)-OCT volumetric images acquired from normal (n = 8) and diabetic (n = 10) mice. The accuracy of the method was assessed by computing the border position errors of the automated segmentation with respect to manual tracings obtained from two experts. The reproducibility was statistically assessed for four retinal layers within eight predefined regions using the mean and SD of the differences in retinal thickness measured in the repeat scans, the coefficient of variation (CV) and the intraclass correlation coefficients (ICC; with 95% confidence intervals [CIs]). RESULTS: The overall mean unsigned border position error for the 10 surfaces computed over 97 B-scans (10 scans, 10 normal mice) was 3.16 ± 0.91 μm. The overall mean differences in retinal thicknesses computed from the normal and diabetic mice were 1.86 ± 0.95 and 2.15 ± 0.86 μm, respectively. The CV of the retinal thicknesses for all the measured layers ranged from 1.04% to 5%. The ICCs for the total retinal thickness in the normal and diabetic mice were 0.78 [0.10, 0.92] and 0.83 [0.31, 0.96], respectively. CONCLUSION: The presented method (publicly available as part of the Iowa Reference Algorithms) has acceptable accuracy and reproducibility and is expected to be useful in the quantitative study of intraretinal layers in mice. TRANSLATIONAL RELEVANCE: The presented method, initially developed for human OCT, has been adapted for mice, with the potential to be adapted for other animals as well. Quantitative in vivo assessment of the retina in mice allows changes to be measured longitudinally, decreasing the need for them.
PURPOSE: To describe an adaptation of an existing graph-theoretic method (initially developed for human optical coherence tomography [OCT] images) for the three-dimensional (3D) automated segmentation of 10 intraretinal surfaces in mice scans, and assess the accuracy of the method and the reproducibility of thickness measurements. METHODS: Ten intraretinal surfaces were segmented in repeat spectral domain (SD)-OCT volumetric images acquired from normal (n = 8) and diabetic (n = 10) mice. The accuracy of the method was assessed by computing the border position errors of the automated segmentation with respect to manual tracings obtained from two experts. The reproducibility was statistically assessed for four retinal layers within eight predefined regions using the mean and SD of the differences in retinal thickness measured in the repeat scans, the coefficient of variation (CV) and the intraclass correlation coefficients (ICC; with 95% confidence intervals [CIs]). RESULTS: The overall mean unsigned border position error for the 10 surfaces computed over 97 B-scans (10 scans, 10 normal mice) was 3.16 ± 0.91 μm. The overall mean differences in retinal thicknesses computed from the normal and diabetic mice were 1.86 ± 0.95 and 2.15 ± 0.86 μm, respectively. The CV of the retinal thicknesses for all the measured layers ranged from 1.04% to 5%. The ICCs for the total retinal thickness in the normal and diabetic mice were 0.78 [0.10, 0.92] and 0.83 [0.31, 0.96], respectively. CONCLUSION: The presented method (publicly available as part of the Iowa Reference Algorithms) has acceptable accuracy and reproducibility and is expected to be useful in the quantitative study of intraretinal layers in mice. TRANSLATIONAL RELEVANCE: The presented method, initially developed for human OCT, has been adapted for mice, with the potential to be adapted for other animals as well. Quantitative in vivo assessment of the retina in mice allows changes to be measured longitudinally, decreasing the need for them.
Authors: Vedran Kajić; Boris Povazay; Boris Hermann; Bernd Hofer; David Marshall; Paul L Rosin; Wolfgang Drexler Journal: Opt Express Date: 2010-07-05 Impact factor: 3.894
Authors: Azadeh Yazdanpanah; Ghassan Hamarneh; Benjamin R Smith; Marinko V Sarunic Journal: IEEE Trans Med Imaging Date: 2010-10-14 Impact factor: 10.048
Authors: Michelle L Gabriele; Hiroshi Ishikawa; Joel S Schuman; Richard A Bilonick; Jongsick Kim; Larry Kagemann; Gadi Wollstein Journal: Invest Ophthalmol Vis Sci Date: 2010-06-23 Impact factor: 4.799
Authors: Ahmet Murat Bagci; Mahnaz Shahidi; Rashid Ansari; Michael Blair; Norman Paul Blair; Ruth Zelkha Journal: Am J Ophthalmol Date: 2008-08-15 Impact factor: 5.258
Authors: Mark E Pennesi; Keith V Michaels; Sienna S Magee; Anastasiya Maricle; Sean P Davin; Anupam K Garg; Michael J Gale; Daniel C Tu; Yuquan Wen; Laura R Erker; Peter J Francis Journal: Invest Ophthalmol Vis Sci Date: 2012-07-10 Impact factor: 4.799
Authors: Elliott H Sohn; Hille W van Dijk; Chunhua Jiao; Pauline H B Kok; Woojin Jeong; Nazli Demirkaya; Allison Garmager; Ferdinand Wit; Murat Kucukevcilioglu; Mirjam E J van Velthoven; J Hans DeVries; Robert F Mullins; Markus H Kuehn; Reinier Otto Schlingemann; Milan Sonka; Frank D Verbraak; Michael David Abràmoff Journal: Proc Natl Acad Sci U S A Date: 2016-04-25 Impact factor: 11.205
Authors: Pengfei Zhang; Azhar Zam; Yifan Jian; Xinlei Wang; Yuanpei Li; Kit S Lam; Marie E Burns; Marinko V Sarunic; Edward N Pugh; Robert J Zawadzki Journal: J Biomed Opt Date: 2015 Impact factor: 3.170
Authors: Bhavna Josephine Antony; Byung-Jin Kim; Andrew Lang; Aaron Carass; Jerry L Prince; Donald J Zack Journal: PLoS One Date: 2017-08-17 Impact factor: 3.240
Authors: Bret A Moore; Michel J Roux; Lionel Sebbag; Ann Cooper; Sydney G Edwards; Brian C Leonard; Denise M Imai; Stephen Griffey; Lynette Bower; Dave Clary; K C Kent Lloyd; Yann Hérault; Sara M Thomasy; Christopher J Murphy; Ala Moshiri Journal: Invest Ophthalmol Vis Sci Date: 2018-05-01 Impact factor: 4.799
Authors: Andrés Cruz-Herranz; Michael Dietrich; Alexander M Hilla; Hao H Yiu; Marc H Levin; Christina Hecker; Andrea Issberner; Angelika Hallenberger; Christian Cordano; Klaus Lehmann-Horn; Lisanne J Balk; Orhan Aktas; Jens Ingwersen; Charlotte von Gall; Hans-Peter Hartung; Scott S Zamvil; Dietmar Fischer; Philipp Albrecht; Ari J Green Journal: J Neuroinflammation Date: 2019-11-04 Impact factor: 8.322
Authors: Adam Hedberg-Buenz; Kacie J Meyer; Carly J van der Heide; Wenxiang Deng; Kyungmoo Lee; Dana A Soukup; Monica Kettelson; Danielle Pellack; Hannah Mercer; Kai Wang; Mona K Garvin; Michael D Abramoff; Michael G Anderson Journal: Transl Vis Sci Technol Date: 2022-09-01 Impact factor: 3.048
Authors: Oliver W Gramlich; Alexander J Brown; Cheyanne R Godwin; Michael S Chimenti; Lauren K Boland; James A Ankrum; Randy H Kardon Journal: Transl Vis Sci Technol Date: 2020-07-10 Impact factor: 3.283