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Panda S, Asman AJ, Delisi MP, et al. Robust Optic Nerve Segmentation on Clinically Acquired CT. Proc SPIE Int Soc Opt Eng. 2014;9034:90341Gdoi: 10.1117/12.2043715.
Panda, S., Asman, A. J., Delisi, M. P., Mawn, L. A., Galloway, R. L., & Landman, B. A. (2014). Robust Optic Nerve Segmentation on Clinically Acquired CT. Proceedings of SPIE--the International Society for Optical Engineering, 903490341G. https://doi.org/10.1117/12.2043715
Panda, Swetasudha, et al. "Robust Optic Nerve Segmentation on Clinically Acquired CT." Proceedings of SPIE--the International Society for Optical Engineering vol. 9034 (2014): 90341G. doi: https://doi.org/10.1117/12.2043715
Panda S, Asman AJ, Delisi MP, Mawn LA, Galloway RL, Landman BA. Robust Optic Nerve Segmentation on Clinically Acquired CT. Proc SPIE Int Soc Opt Eng. 2014 Mar 21;9034:90341G. doi: 10.1117/12.2043715. PMID: 24817810; PMCID: PMC4013110.
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Proc SPIE Int Soc Opt Eng. 2014 Mar 21;9034:90341G. doi: 10.1117/12.2043715.

Robust Optic Nerve Segmentation on Clinically Acquired CT.

Proceedings of SPIE--the International Society for Optical Engineering

Swetasudha Panda, Andrew J Asman, Michael P Delisi, Louise A Mawn, Robert L Galloway, Bennett A Landman

Affiliations

  1. Electrical Engineering, Vanderbilt University, Nashville, TN, USA 37235.
  2. Biomedical Engineering, Vanderbilt University, Nashville, TN, USA 37235.
  3. Ophthalmology and Neurological Surgery, Vanderbilt University, Nashville, TN, USA 37235.
  4. Electrical Engineering, Vanderbilt University, Nashville, TN, USA 37235 ; Biomedical Engineering, Vanderbilt University, Nashville, TN, USA 37235.

PMID: 24817810 PMCID: PMC4013110 DOI: 10.1117/12.2043715

Abstract

The optic nerve is a sensitive central nervous system structure, which plays a critical role in many devastating pathological conditions. Several methods have been proposed in recent years to segment the optic nerve automatically, but progress toward full automation has been limited. Multi-atlas methods have been successful for brain segmentation, but their application to smaller anatomies remains relatively unexplored. Herein we evaluate a framework for robust and fully automated segmentation of the optic nerves, eye globes and muscles. We employ a robust registration procedure for accurate registrations, variable voxel resolution and image field-of-view. We demonstrate the efficacy of an optimal combination of SyN registration and a recently proposed label fusion algorithm (Non-local Spatial STAPLE) that accounts for small-scale errors in registration correspondence. On a dataset containing 30 highly varying computed tomography (CT) images of the human brain, the optimal registration and label fusion pipeline resulted in a median Dice similarity coefficient of 0.77, symmetric mean surface distance error of 0.55 mm, symmetric Hausdorff distance error of 3.33 mm for the optic nerves. Simultaneously, we demonstrate the robustness of the optimal algorithm by segmenting the optic nerve structure in 316 CT scans obtained from 182 subjects from a thyroid eye disease (TED) patient population.

Keywords: Computed Tomography; Multi-Atlas; Optic Nerve; Segmentation; Statistical Label Fusion

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