Image Processing

Evaluation of multiatlas label fusion for in vivo magnetic resonance imaging orbital segmentation

[+] Author Affiliations
Swetasudha Panda, Andrew J. Asman

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

Shweta P. Khare

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

Lindsey Thompson

Vanderbilt University, Institute of Imaging Science, Nashville, Tennessee 37235, United States

Louise A. Mawn

Vanderbilt University, Department of Ophthalmology and Neurological Surgery, Nashville, Tennessee 37232, United States

Seth A. Smith

Vanderbilt University, Institute of Imaging Science, Nashville, Tennessee 37235, United States

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

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

J. Med. Imag. 1(2), 024002 (Jul 18, 2014). doi:10.1117/1.JMI.1.2.024002
History: Received October 29, 2013; Revised May 15, 2014; Accepted June 24, 2014
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Abstract.  Multiatlas methods have been successful for brain segmentation, but their application to smaller anatomies remains relatively unexplored. We evaluate seven statistical and voting-based label fusion algorithms (and six additional variants) to segment the optic nerves, eye globes, and chiasm. For nonlocal simultaneous truth and performance level estimation (STAPLE), we evaluate different intensity similarity measures (including mean square difference, locally normalized cross-correlation, and a hybrid approach). Each algorithm is evaluated in terms of the Dice overlap and symmetric surface distance metrics. Finally, we evaluate refinement of label fusion results using a learning-based correction method for consistent bias correction and Markov random field regularization. The multiatlas labeling pipelines were evaluated on a cohort of 35 subjects including both healthy controls and patients. Across all three structures, nonlocal spatial STAPLE (NLSS) with a mixed weighting type provided the most consistent results; for the optic nerve NLSS resulted in a median Dice similarity coefficient of 0.81, mean surface distance of 0.41 mm, and Hausdorff distance 2.18 mm for the optic nerves. Joint label fusion resulted in slightly superior median performance for the optic nerves (0.82, 0.39 mm, and 2.15 mm), but slightly worse on the globes. The fully automated multiatlas labeling approach provides robust segmentations of orbital structures on magnetic resonance imaging even in patients for whom significant atrophy (optic nerve head drusen) or inflammation (multiple sclerosis) is present.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Swetasudha Panda ; Andrew J. Asman ; Shweta P. Khare ; Lindsey Thompson ; Louise A. Mawn, et al.
"Evaluation of multiatlas label fusion for in vivo magnetic resonance imaging orbital segmentation", J. Med. Imag. 1(2), 024002 (Jul 18, 2014). ; http://dx.doi.org/10.1117/1.JMI.1.2.024002


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