An adaptive grid for graph-based segmentation in retinal OCT

Andrew Lang; Aaron Carass; Peter A. Calabresi; Howard S. Ying; Jerry L. Prince

doi:10.1117/12.2043040

21 March 2014 An adaptive grid for graph-based segmentation in retinal OCT

Andrew Lang, Aaron Carass, Peter A. Calabresi, Howard S. Ying, Jerry L. Prince

Proceedings Volume 9034, Medical Imaging 2014: Image Processing; 903402 (2014) https://doi.org/10.1117/12.2043040
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Graph-based methods for retinal layer segmentation have proven to be popular due to their efficiency and accuracy. These methods build a graph with nodes at each voxel location and use edges connecting nodes to encode the hard constraints of each layer's thickness and smoothness. In this work, we explore deforming the regular voxel grid to allow adjacent vertices in the graph to more closely follow the natural curvature of the retina. This deformed grid is constructed by fixing node locations based on a regression model of each layer's thickness relative to the overall retina thickness, thus we generate a subject specific grid. Graph vertices are not at voxel locations, which allows for control over the resolution that the graph represents. By incorporating soft constraints between adjacent nodes, segmentation on this grid will favor smoothly varying surfaces consistent with the shape of the retina. Our final segmentation method then follows our previous work. Boundary probabilities are estimated using a random forest classifier followed by an optimal graph search algorithm on the new adaptive grid to produce a final segmentation. Our method is shown to produce a more consistent segmentation with an overall accuracy of 3.38 μm across all boundaries.

Citation Download Citation

Andrew Lang, Aaron Carass, Peter A. Calabresi, Howard S. Ying, and Jerry L. Prince "An adaptive grid for graph-based segmentation in retinal OCT", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 903402 (21 March 2014); https://doi.org/10.1117/12.2043040

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