Image Processing

Robust optic nerve segmentation on clinically acquired computed tomography

[+] Author Affiliations
Robert L. Harrigan

Vanderbilt University, Department of Electrical Engineering, Nashville, Tennessee 37235, United States

Swetasudha Panda, Andrew J. Asman, Katrina M. Nelson, Benjamin C. W. Yvernault

Vanderbilt University, Department of Electrical Engineering, Nashville, Tennessee 37235, United States

Shikha Chaganti

Vanderbilt University, Department of Computer Science, Nashville, Tennessee 37235, United States

Michael P. DeLisi, Robert L. Galloway

Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee 37235, United States

Seth A. Smith

Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee 37235, United States

Vanderbilt University, Department of Radiology and Radiological Sciences, Nashville, Tennessee 37235, United States

Louise A. Mawn

Vanderbilt University, Department of Ophthalmology and Neurological Surgery, Nashville, Tennessee 37235, United States

Bennett A. Landman

Vanderbilt University, Department of Electrical Engineering, Nashville, Tennessee 37235, United States

Vanderbilt University, Department of Computer Science, Nashville, Tennessee 37235, United States

Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee 37235, United States

Vanderbilt University, Department of Radiology and Radiological Sciences, Nashville, Tennessee 37235, United States

J. Med. Imag. 1(3), 034006 (Dec 17, 2014). doi:10.1117/1.JMI.1.3.034006
History: Received August 8, 2014; Accepted November 17, 2014
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Abstract.  The optic nerve (ON) plays a critical role in many devastating pathological conditions. Segmentation of the ON has the ability to provide understanding of anatomical development and progression of diseases of the ON. Recently, methods have been proposed to segment the ON but progress toward full automation has been limited. We optimize registration and fusion methods for a new multi-atlas framework for automated segmentation of the ONs, eye globes, and muscles on clinically acquired computed tomography (CT) data. Briefly, the multi-atlas approach consists of determining a region of interest within each scan using affine registration, followed by nonrigid registration on reduced field of view atlases, and performing statistical fusion on the results. We evaluate the robustness of the approach by segmenting the ON structure in 501 clinically acquired CT scan volumes obtained from 183 subjects from a thyroid eye disease patient population. A subset of 30 scan volumes was manually labeled to assess accuracy and guide method choice. Of the 18 compared methods, the ANTS Symmetric Normalization registration and nonlocal spatial simultaneous truth and performance level estimation statistical fusion resulted in the best overall performance, resulting in a median Dice similarity coefficient of 0.77, which is comparable with inter-rater (human) reproducibility at 0.73.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Robert L. Harrigan ; Swetasudha Panda ; Andrew J. Asman ; Katrina M. Nelson ; Shikha Chaganti, et al.
"Robust optic nerve segmentation on clinically acquired computed tomography", J. Med. Imag. 1(3), 034006 (Dec 17, 2014). ; http://dx.doi.org/10.1117/1.JMI.1.3.034006


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