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Journal of Medical Imaging

Efficient orbital structures segmentation with prior anatomical knowledge
Author(s): Nava Aghdasi; Yangming Li; Angelique M. Berens; Richard A. Harbison; Kris S. Moe; Blake Hannaford
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Paper Abstract

We present a fully automatic method for segmenting orbital structures (globes, optic nerves, and extraocular muscles) in CT images. Prior anatomical knowledge, such as shape, intensity, and spatial relationships of organs and landmarks, were utilized to define a volume of interest (VOI) that contains the desired structures. Then, VOI was used for fast localization and successful segmentation of each structure using predefined rules. Testing our method with 30 publicly available datasets, the average Dice similarity coefficient for right and left sides of [0.81, 0.79] eye globes, [0.72, 0.79] optic nerves, and [0.73, 0.76] extraocular muscles were achieved. The proposed method is accurate, efficient, does not require training data, and its intuitive pipeline allows the user to modify or extend to other structures.

Paper Details

Date Published: 22 July 2017
PDF: 12 pages
J. Med. Imag. 4(3) 034501 doi: 10.1117/1.JMI.4.3.034501
Published in: Journal of Medical Imaging Volume 4, Issue 3
Show Author Affiliations
Nava Aghdasi, Univ. of Washington (United States)
Yangming Li, Univ. of Washington (United States)
Angelique M. Berens, Univ. of Washington (United States)
Richard A. Harbison, Univ. of Washington (United States)
Kris S. Moe, Univ. of Washington (United States)
Blake Hannaford, Univ. of Washington (United States)

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