TY - GEN
T1 - Multi-layer fast level set segmentation for macular OCT
AU - Liu, Yihao
AU - Carass, Aaron
AU - Solomon, Sharon D.
AU - Saidha, Shiv
AU - Calabresi, Peter A.
AU - Prince, Jerry L.
N1 - Funding Information:
This work was supported by the NIH/NEI under grant R01-EY024655 (PI: Prince) and by the NIH/NINDS under grant R01-NS082347 (PI: Calabresi).
Publisher Copyright:
© 2018 IEEE.
PY - 2018/5/23
Y1 - 2018/5/23
N2 - Segmenting optical coherence tomography (OCT) images of the retina is important in the diagnosis, staging, and tracking of ophthalmological diseases. Whereas automatic segmentation methods are typically much faster than manual segmentation, they may still take several minutes to segment a three-dimensional macular scan, and this can be prohibitive for routine clinical application. In this paper, we propose a fast, multi-layer macular OCT segmentation method based on a fast level set method. In our framework, the boundary evolution operations are computationally fast, are specific to each boundary between retinal layers, guarantee proper layer ordering, and avoid level set computation during evolution. Subvoxel resolution is achieved by reconstructing the level set functions after convergence. Experiments demonstrate that our method reduces the computation expense by 90% compared to graph-based methods and produces comparable accuracy to both graph-based and level set retinal OCT segmentation methods.
AB - Segmenting optical coherence tomography (OCT) images of the retina is important in the diagnosis, staging, and tracking of ophthalmological diseases. Whereas automatic segmentation methods are typically much faster than manual segmentation, they may still take several minutes to segment a three-dimensional macular scan, and this can be prohibitive for routine clinical application. In this paper, we propose a fast, multi-layer macular OCT segmentation method based on a fast level set method. In our framework, the boundary evolution operations are computationally fast, are specific to each boundary between retinal layers, guarantee proper layer ordering, and avoid level set computation during evolution. Subvoxel resolution is achieved by reconstructing the level set functions after convergence. Experiments demonstrate that our method reduces the computation expense by 90% compared to graph-based methods and produces comparable accuracy to both graph-based and level set retinal OCT segmentation methods.
KW - Fast level set method
KW - Multi-object segmentation
KW - OCT
KW - Topology preservation
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U2 - 10.1109/ISBI.2018.8363844
DO - 10.1109/ISBI.2018.8363844
M3 - Conference contribution
AN - SCOPUS:85048125753
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 1445
EP - 1448
BT - 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PB - IEEE Computer Society
T2 - 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
Y2 - 4 April 2018 through 7 April 2018
ER -