Automated Bruch’s Membrane Opening Segmentation in Cases of Optic Disc Swelling in Combined 2D and 3D SD-OCT Images Using Shape-Prior and Texture Information
When the optic disc is swollen, the visibility of the Bruch’s membrane opening (BMO) is often drastically reduced in spectral-domain optical coherence tomography (SD-OCT) volumes. Recent work pro- posed a semi-automated method to segment the BMO using combined information from 2D high-definition raster and 3D volumetric SD-OCT scans; however, manual placement of six landmark points was required. In this work, we propose a fully automated approach to segment the BMO from 2D high-definition and 3D volumetric SD-OCT scans. Using the topographic shape of the internal limiting membrane and textural information near Bruch’s membrane, two BMO points are first estimated in the high-definition central B-scan and then registered into the corresponding volumetric scan. Utilizing the information from both the high- definition BMO estimates and the standard-definition SD-OCT volume, the cost image was created. A graph-based algorithm with soft shape-based constraints is further applied to segment the BMO contour on the SD-OCT en-face image domain. Using a set of 23 volumes with reasonably centered raster scans and swelling larger than 14.42 mm3, the fully automated approach was significantly more accurate than a traditional approach utilizing information only from the SD-OCT volume (RMS error of 7.18 vs. 21.37 in pixels; p < 0.05) and had only a slightly higher (and not significantly different) error than the previously proposed semi-automated approach (RMS error of 7.18 vs. 5.30 in pixels; p = 0.08).
How to Cite:
Wang, J. & Kardon, R. H. & Garvin, M. K., (2015) “Automated Bruch’s Membrane Opening Segmentation in Cases of Optic Disc Swelling in Combined 2D and 3D SD-OCT Images Using Shape-Prior and Texture Information”, Proceedings of the Ophthalmic Medical Image Analysis International Workshop 2(2015), 33-40. doi: https://doi.org/10.17077/omia.1024
Rights: Copyright © 2015 Jui-Kai Wang, Randy H. Kardon, and Mona K. Garvin