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David Xu, Alex Yuan, Peter K. Kaiser, Sunil K. Srivastava, Rishi P. Singh, Jonathan E. Sears, Daniel F. Martin, Justis P. Ehlers; A Novel Segmentation Algorithm for Volumetric Analysis of Macular Hole Boundaries Identified with Optical Coherence Tomography. Invest. Ophthalmol. Vis. Sci. 2013;54(1):163-169. doi: https://doi.org/10.1167/iovs.12-10246.
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© ARVO (1962-2015); The Authors (2016-present)
To demonstrate a novel algorithm for macular hole (MH) segmentation and volumetric analysis.
A computer algorithm was developed for automated MH segmentation in spectral-domain optical coherence tomography (SD-OCT). Algorithm validation was performed by trained graders with performance characterized by absolute accuracy and intraclass correlation coefficient. A retrospective case series of 56 eyes of 55 patients with idiopathic MHs analyzed using the custom algorithm to measure MH volume, base area/diameter, top area/diameter, minimum diameter, and height-to-base diameter ratio. Five eyes were excluded due to poor signal quality (1), motion artifact (1), and failure of surgical closure (3) for a final cohort of 51 eyes. Preoperative MH measurements were correlated with clinical MH stage, baseline, and 6-month postoperative best-corrected Snellen visual acuity (BCVA).
The algorithm achieved 96% absolute accuracy and an intraclass correlation of 0.994 compared to trained graders. In univariate analysis, MH volume, base area, base diameter, top area, top diameter, minimum diameter, and MH height were significantly correlated to baseline BCVA (P value from 0.0003–0.011). Volume, base area, base diameter, and height–to-base diameter ratio were significantly correlated to 6-month postoperative BCVA (P value from <0.0001–0.029). In multivariate analysis, only base area (P < 0.0001) and volume (P = 0.0028) were significant predictors of 6-month postoperative BCVA.
The computerized segmentation algorithm enables rapid volumetric analysis of MH geometry and correlates with baseline and postoperative visual function. Further research is needed to better understand the algorithm's role in prognostication and clinical management.
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