4D Graph-Based Segmentation for Reproducible and Sensitive Choroid Quantification From Longitudinal OCT Scans
Abramoff, Michael D.
Zhang, Ellen Ziyi
Sonka, MilanNote: Order does not necessarily reflect citation order of authors.
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CitationOguz, Ipek, Michael D. Abramoff, Li Zhang, Kyungmoo Lee, Ellen Ziyi Zhang, and Milan Sonka. 2016. “4D Graph-Based Segmentation for Reproducible and Sensitive Choroid Quantification From Longitudinal OCT Scans.” Investigative Ophthalmology & Visual Science 57 (9): OCT621-OCT630. doi:10.1167/iovs.15-18924. http://dx.doi.org/10.1167/iovs.15-18924.
AbstractPurpose Longitudinal imaging is becoming more commonplace for studies of disease progression, response to treatment, and healthy maturation. Accurate and reproducible quantification methods are desirable to fully mine the wealth of data in such datasets. However, most current retinal OCT segmentation methods are cross-sectional and fail to leverage the inherent context present in longitudinal sequences of images. Methods: We propose a novel graph-based method for segmentation of multiple three-dimensional (3D) scans over time (termed 3D + time or 4D). The usefulness of this approach in retinal imaging is illustrated in the segmentation of the choroidal surfaces from longitudinal optical coherence tomography (OCT) scans. A total of 3219 synthetic (3070) and patient (149) OCT images were segmented for validation of our approach. Results: The results show that the proposed 4D segmentation method is significantly more reproducible (P < 0.001) than the 3D approach and is significantly more sensitive to temporal changes (P < 0.0001) achieved by the substantial increase of measurement robustness. Conclusions: This is the first automated 4D method for jointly quantifying choroidal thickness in longitudinal OCT studies. Our method is robust to image noise and produces more reproducible choroidal thickness measurements than a sequence of independent 3D segmentations, without sacrificing sensitivity to temporal changes.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:30371062
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