Journal article
Automated Segmentability Index for Layer Segmentation of Macular SD-OCT Images
Translational vision science & technology, Vol.5(2), pp.1-12
04/05/2016
DOI: 10.1167/tvst.5.2.14
PMID: 27066311
Abstract
Purpose: To automatically identify which spectral-domain optical coherence tomography (SD-OCT) scans will provide reliable automated layer segmentations for more accurate layer thickness analyses in population studies.
Methods: Six hundred ninety macular SD-OCT image volumes (6.0 x 6.0 x 2.3 mm(3)) were obtained from one eyes of 690 subjects (74.6 +/- 9.7 [mean +/- SD] years, 37.8% of males) randomly selected from the population-based Rotterdam Study. The dataset consisted of 420 OCT volumes with successful automated retinal nerve fiber layer (RNFL) segmentations obtained from our previously reported graph-based segmentation method and 270 volumes with failed segmentations. To evaluate the reliability of the layer segmentations, we have developed a new metric, segmentability index SI, which is obtained from a random forest regressor based on 12 features using OCT voxel intensities, edge-based costs, and on-surface costs. The SI was compared with well-known quality indices, quality index (QI), and maximum tissue contrast index (mTCI), using receiver operating characteristic (ROC) analysis.
Results: The 95% confidence interval (CI) and the area under the curve (AUC) for the QI are 0.621 to 0.805 with AUC 0.713, for the mTCI 0.673 to 0.838 with AUC 0.756, and for the SI 0.784 to 0.920 with AUC 0.852. The SI AUC is significantly larger than either the QI or mTCI AUC (P < 0.01).
Conclusions: The segmentability index SI is well suited to identify SD-OCT scans for which successful automated intraretinal layer segmentations can be expected.
Translational Relevance: Interpreting the quantification of SD-OCT images requires the underlying segmentation to be reliable, but standard SD-OCT quality metrics do not predict which segmentations are reliable and which are not. The segmentability index SI presented in this study does allow reliable segmentations to be identified, which is important for more accurate layer thickness analyses in research and population studies.
Details
- Title: Subtitle
- Automated Segmentability Index for Layer Segmentation of Macular SD-OCT Images
- Creators
- Kyungmoo Lee - Univ Iowa, Dept Elect & Comp Engn, Iowa City, IA 52242 USAGabrielle H. S. Buitendijk - Erasmus MCHrvoje Bogunovic - Univ Iowa, Dept Elect & Comp Engn, Iowa City, IA 52242 USAHenriet Springelkamp - Erasmus MC, Dept Ophthalmol, Rotterdam, NetherlandsAlbert Hofman - Erasmus University RotterdamAndreas Wahle - University of Iowa, The Iowa Institute for Biomedical ImagingMilan Sonka - University of Iowa, Fraternal Order of Eagles Diabetes Research CenterJohannes R. Vingerling - Erasmus MC, Dept Ophthalmol, Rotterdam, NetherlandsCaroline C. W. Klaver - Erasmus MCMichael D. Abramoff - University of Iowa, Fraternal Order of Eagles Diabetes Research Center
- Resource Type
- Journal article
- Publication Details
- Translational vision science & technology, Vol.5(2), pp.1-12
- DOI
- 10.1167/tvst.5.2.14
- PMID
- 27066311
- NLM abbreviation
- Transl Vis Sci Technol
- ISSN
- 2164-2591
- eISSN
- 2164-2591
- Publisher
- Assoc Research Vision Ophthalmology Inc
- Number of pages
- 12
- Grant note
- Rotterdamse Blindenbelangen Association, Rotterdam R01EY017066; R01EY018853 / NATIONAL EYE INSTITUTE; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Eye Institute (NEI) Algemene Nederlandse Vereniging ter Voorkoming van Blindheid, Doorn, The Netherlands; Netherlands Government Oogfonds Nederland, Utrecht Netherlands Organization for Scientific Research, the Hague; Netherlands Organization for Scientific Research (NWO) Topcon Europe BV, Capelle aan den IJssel, The Netherlands Bevordering van Volkskracht, Rotterdam Department of Veterans Affairs; US Department of Veterans Affairs MDFonds, Utrecht R01EB004640 / NATIONAL INSTITUTE OF BIOMEDICAL IMAGING AND BIOENGINEERING; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of Biomedical Imaging & Bioengineering (NIBIB)
- Language
- English
- Date published
- 04/05/2016
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Radiation Oncology; Injury Prevention Research Center; Ophthalmology and Visual Sciences
- Record Identifier
- 9983806264302771
Metrics
33 Record Views