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Proceedings Paper

Multiresolution LOGISMOS graph search for automated choroidal layer segmentation of 3D macular OCT scans
Author(s): Kyungmoo Lee; Elliott H. Sohn; Honghai Zhang; Alexis K. Warren; Andreas Wahle; S. Scott Whitmore; Milan Sonka; Michael D. Abràmoff
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Paper Abstract

The choroid is a vascular plexus located between the retina and the sclera, providing oxygen and nourishment to the outer layers of the retina. Thickness changes in the choroid are of importance in the pathophysiology of various ocular diseases such as glaucoma, age-related macular degeneration (AMD), and others. Our previous choroidal layer segmentation method of 3D macular optical coherence tomography (OCT) scans using choroidal vessel segmentation tended to segment thinner choroidal layers than ground truths, requiring long running time and much memory. To overcome these drawbacks, we introduce a new, fast, and memory-efficient multiresolution LOGISMOS (layered optimal graph image segmentation for multiple objects and surfaces) method. The key idea of the method is to consequently segment the choroidal layer in the higher resolution sub-OCT image volume constrained by the layer segmented in the lower resolution OCT image volume to reduce the size of columns for graph search. Generally, it outperformed our previous method and showed a similar performance to the inter-observer variability between 2 experts.

Paper Details

Date Published: 10 March 2020
PDF: 6 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113130B (10 March 2020); doi: 10.1117/12.2549322
Show Author Affiliations
Kyungmoo Lee, The Univ. of Iowa (United States)
Elliott H. Sohn, The Univ. of Iowa (United States)
Honghai Zhang, The Univ. of Iowa (United States)
Alexis K. Warren, The Univ. of Iowa (United States)
Andreas Wahle, The Univ. of Iowa (United States)
S. Scott Whitmore, The Univ. of Iowa (United States)
Milan Sonka, The Univ. of Iowa (United States)
Michael D. Abràmoff, The Univ. of Iowa (United States)

Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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