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Abstract #1104

Robust Segmentation of Clinical Optic Nerve MRI

Swetasudha Panda1, Andrew J. Asman1, Bennett A. Landman1, 2, Seth A. Smith2, Louise A. Mawn3

1Electrical Engineering, Vanderbilt University, Nashville, TN, United States; 2Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, United States; 3Ophthalmology and Neurological Surgery, Vanderbilt University, Nashville, TN, United States

We aim to develop tools to automatically quantify the location and volumetrics of the optic nerve for integration of multimodal imaging data. We hypothesize that this will increase sensitivity and specificity of pathology assessments relative to coarse, manual region of interest approaches. Manual segmentation struggles significantly in the optic nerve when pathology is present or in the later stages of optic nerve damage. While multi-atlas segmentation promises a robust and model-free approach to segment medical images from exemplar images for brain structures, extrapolation to smaller structures of the human anatomy have largely been unexplored. Our purpose is to extend and evaluate multi-atlas labeling for the segmentation of the optic nerve based on high-resolution T2-weighted MRI of the optic nerve.