Kyungmoo Lee1, Alexis K Warren2, Michael D Abràmoff3, Andreas Wahle1, S Scott Whitmore4, Ian C Han4, John H Fingert4, Todd E Scheetz5, Robert F Mullins4, Milan Sonka6, Elliott H Sohn7. 1. Iowa Institute for Biomedical Imaging, University of Iowa, Iowa City, IA, United States; Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, United States. 2. Department of Ophthalmology and Visual Sciences, University of Iowa Hospitals and Clinics, Iowa City, IA, United States. 3. Iowa Institute for Biomedical Imaging, University of Iowa, Iowa City, IA, United States; Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, United States; Department of Ophthalmology and Visual Sciences, University of Iowa Hospitals and Clinics, Iowa City, IA, United States; Department of Biomedical Engineering, University of Iowa, Iowa City, IA, United States; Institute for Vision Research, University of Iowa, Iowa City, IA, United States; Veterans Affairs Medical Center, Iowa City, IA, United States; IDx, Coralville, IA, United States. 4. Department of Ophthalmology and Visual Sciences, University of Iowa Hospitals and Clinics, Iowa City, IA, United States; Institute for Vision Research, University of Iowa, Iowa City, IA, United States. 5. Iowa Institute for Biomedical Imaging, University of Iowa, Iowa City, IA, United States; Department of Ophthalmology and Visual Sciences, University of Iowa Hospitals and Clinics, Iowa City, IA, United States; Department of Biomedical Engineering, University of Iowa, Iowa City, IA, United States; Institute for Vision Research, University of Iowa, Iowa City, IA, United States. 6. Iowa Institute for Biomedical Imaging, University of Iowa, Iowa City, IA, United States; Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, United States; Department of Ophthalmology and Visual Sciences, University of Iowa Hospitals and Clinics, Iowa City, IA, United States. 7. Department of Ophthalmology and Visual Sciences, University of Iowa Hospitals and Clinics, Iowa City, IA, United States; Institute for Vision Research, University of Iowa, Iowa City, IA, United States. Electronic address: Elliott.sohn@gmail.com.
Abstract
BACKGROUND: Changes in choroidal thickness are associated with various ocular diseases, and the choroid can be imaged using spectral-domain optical coherence tomography (SD-OCT) and enhanced depth imaging OCT (EDI-OCT). NEW METHOD: Eighty macular SD-OCT volumes from 80 patients were obtained using the Zeiss Cirrus machine. Eleven additional control subjects had two Cirrus scans done in one visit along with enhanced depth imaging (EDI-OCT) using the Heidelberg Spectralis machine. To automatically segment choroidal layers from the OCT volumes, our graph-theoretic approach was utilized. The segmentation results were compared with reference standards from two independent graders, and the accuracy of automated segmentation was calculated using unsigned/signed border positioning/thickness errors and Dice similarity coefficient (DSC). The repeatability and reproducibility of our choroidal thicknesses were determined by intraclass correlation coefficient (ICC), coefficient of variation (CV), and repeatability coefficient (RC). RESULTS: The mean unsigned/signed border positioning errors for the choroidal inner and outer surfaces are 3.39 ± 1.26 µm (mean ± standard deviation)/- 1.52 ± 1.63 µm and 16.09 ± 6.21 µm/4.73 ± 9.53 µm, respectively. The mean unsigned/signed choroidal thickness errors are 16.54 ± 6.47 µm/6.25 ± 9.91 µm, and the mean DSC is 0.949 ± 0.025. The ICC (95% confidence interval), CV, RC values are 0.991 (0.977-0.997), 2.48%, 14.25 µm for the repeatability and 0.991 (0.977-0.997), 2.49%, 14.30 µm for the reproducibility studies, respectively. COMPARISON WITH EXISTING METHOD(S): The proposed method outperformed our previous method using choroidal vessel segmentation and inter-grader variability. CONCLUSIONS: This automated segmentation method can reliably measure choroidal thickness using different OCT platforms.
BACKGROUND: Changes in choroidal thickness are associated with various ocular diseases, and the choroid can be imaged using spectral-domain optical coherence tomography (SD-OCT) and enhanced depth imaging OCT (EDI-OCT). NEW METHOD: Eighty macular SD-OCT volumes from 80 patients were obtained using the Zeiss Cirrus machine. Eleven additional control subjects had two Cirrus scans done in one visit along with enhanced depth imaging (EDI-OCT) using the Heidelberg Spectralis machine. To automatically segment choroidal layers from the OCT volumes, our graph-theoretic approach was utilized. The segmentation results were compared with reference standards from two independent graders, and the accuracy of automated segmentation was calculated using unsigned/signed border positioning/thickness errors and Dice similarity coefficient (DSC). The repeatability and reproducibility of our choroidal thicknesses were determined by intraclass correlation coefficient (ICC), coefficient of variation (CV), and repeatability coefficient (RC). RESULTS: The mean unsigned/signed border positioning errors for the choroidal inner and outer surfaces are 3.39 ± 1.26 µm (mean ± standard deviation)/- 1.52 ± 1.63 µm and 16.09 ± 6.21 µm/4.73 ± 9.53 µm, respectively. The mean unsigned/signed choroidal thickness errors are 16.54 ± 6.47 µm/6.25 ± 9.91 µm, and the mean DSC is 0.949 ± 0.025. The ICC (95% confidence interval), CV, RC values are 0.991 (0.977-0.997), 2.48%, 14.25 µm for the repeatability and 0.991 (0.977-0.997), 2.49%, 14.30 µm for the reproducibility studies, respectively. COMPARISON WITH EXISTING METHOD(S): The proposed method outperformed our previous method using choroidal vessel segmentation and inter-grader variability. CONCLUSIONS: This automated segmentation method can reliably measure choroidal thickness using different OCT platforms.
Authors: S Scott Whitmore; Elliott H Sohn; Kathleen R Chirco; Arlene V Drack; Edwin M Stone; Budd A Tucker; Robert F Mullins Journal: Prog Retin Eye Res Date: 2014-12-05 Impact factor: 21.198
Authors: Li Zhang; Kyungmoo Lee; Meindert Niemeijer; Robert F Mullins; Milan Sonka; Michael D Abràmoff Journal: Invest Ophthalmol Vis Sci Date: 2012-11-01 Impact factor: 4.799
Authors: Eric K Chin; David Rp Almeida; C Nathaniel Roybal; Philip I Niles; Karen M Gehrs; Elliott H Sohn; H Culver Boldt; Stephen R Russell; James C Folk Journal: Clin Ophthalmol Date: 2015-08-11