A MULTI-STAGE APPROACH FOR RETINAL LAYER SEGMENTATION IN OCT B-SCAN IMAGES
Received: 2026-07-15 15:37:26
Published: 2026-04-18
Abstract
This study introduces an interpretable, training-free, and computationally efficient multi-stage framework for the segmentation of retinal layers in OCT B-scan images. The proposed approach is robust to common challenges such as speckle noise, depth-related illumination inconsistencies, and variations across imaging devices and acquisition protocols, while maintaining fast execution on standard CPU systems. Evaluation is carried out using clinically relevant metrics, including Dice coefficient, IoU, Boundary F1-score, and layer thickness error, along with practical recommendations for parameter tuning. The method demonstrates consistent and reliable performance, achieving an effective balance between quality, computational complexity, and resource consumption, making it suitable for environments with limited computational capacity.
Keywords
List of references
-
Huang D., Swanson E.A., Lin C.P., Schuman J.S., Stinson W.G., Chang W., Hee M.R., Flotte T., Gregory K., Puliafito C.A., Fujimoto J.G. Optical coherence tomography // Science. – 1991. – Vol. 254, No. 5035. – P. 1178–1181.
-
Ronneberger O., Fischer P., Brox T. U-net: Convolutional networks for biomedical image segmentation // Medical Image Computing and Computer-Assisted Intervention (MICCAI). – 2015. – Vol. 9351. – P. 234–241.
-
Badrinarayanan V., Kendall A., Cipolla R. Segnet: A deep convolutional encoder-decoder architecture for image segmentation // IEEE Transactions on Pattern Analysis and Machine Intelligence. – 2017. – Vol. 39, No. 12. – P. 2481–2495.
-
Oktay O., Schlemper J., Le Folgoc L., Lee M., Heinrich M., Misawa K., Mori K., McDonagh S., Hammerla N.Y., Kainz B., Glocker B., Rueckert D. Attention u-net: Learning where to look for the pancreas // arXiv preprint arXiv:1804.03999. – 2018.
-
Zuiderveld K. Contrast limited adaptive histogram equalization // Graphics Gems IV / ed. Heckbert P.S. – San Diego: Academic Press, 1994. – P. 474–485.
-
Yu Y., Acton S.T. Speckle reducing anisotropic diffusion // IEEE Transactions on Image Processing. – 2002. – Vol. 11, No. 11. – P. 1260–1270.
-
Canny J. A computational approach to edge detection // IEEE Transactions on Pattern Analysis and Machine Intelligence. – 1986. – Vol. 8, No. 6. – P. 679–698.
-
Vincent L., Soille P. Watersheds in digital spaces: An efficient algorithm based on immersion simulations // IEEE Transactions on Pattern Analysis and Machine Intelligence. – 1991. – Vol. 13, No. 6. – P. 583–598.
-
Arbelaez P., Maire M., Fowlkes C., Malik J. Contour detection and hierarchical image segmentation // IEEE Transactions on Pattern Analysis and Machine Intelligence. – 2011. – Vol. 33, No. 5. – P. 898–916.
-
Otsu N. A threshold selection method from gray-level histograms // IEEE Transactions on Systems, Man, and Cybernetics. – 1979. – Vol. 9, No. 1. – P. 62–66.
About the Authors
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
