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20 March 2015 Fast and memory-efficient LOGISMOS graph search for intraretinal layer segmentation of 3D macular OCT scans
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Abstract
Image segmentation is important for quantitative analysis of medical image data. Recently, our research group has introduced a 3-D graph search method which can simultaneously segment optimal interacting surfaces with respect to the cost function in volumetric images. Although it provides excellent segmentation accuracy, it is computationally demanding (both CPU and memory) to simultaneously segment multiple surfaces from large volumetric images. Therefore, we propose a new, fast, and memory-efficient graph search method for intraretinal layer segmentation of 3-D macular optical coherence tomograpy (OCT) scans. The key idea is to reduce the size of a graph by combining the nodes with high costs based on the multiscale approach. The new approach requires significantly less memory and achieves significantly faster processing speeds (p < 0.01) with only small segmentation differences compared to the original graph search method. This paper discusses sub-optimality of this approach and assesses trade-off relationships between decreasing processing speed and increasing segmentation differences from that of the original method as a function of employed scale of the underlying graph construction.
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Kyungmoo Lee, Li Zhang, Michael D. Abramoff M.D., and Milan Sonka "Fast and memory-efficient LOGISMOS graph search for intraretinal layer segmentation of 3D macular OCT scans", Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94133X (20 March 2015); https://doi.org/10.1117/12.2082135
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