Efficient orbital structures segmentation with prior anatomical knowledge

Nava Aghdasi; Yangming Li; Angelique M. Berens M.D.; Richard A. Harbison M.D.; Kris S. Moe M.D.; Blake Hannaford

doi:10.1117/1.JMI.4.3.034501

22 July 2017 Efficient orbital structures segmentation with prior anatomical knowledge

Nava Aghdasi, Yangming Li, Angelique M. Berens M.D., Richard A. Harbison M.D., Kris S. Moe M.D., Blake Hannaford

Author Affiliations +

Journal of Medical Imaging, Vol. 4, Issue 3, 034501 (July 2017). https://doi.org/10.1117/1.JMI.4.3.034501

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.

Citation Download Citation

Nava Aghdasi, Yangming Li, Angelique M. Berens M.D., Richard A. Harbison M.D., Kris S. Moe M.D., and Blake Hannaford "Efficient orbital structures segmentation with prior anatomical knowledge," Journal of Medical Imaging 4(3), 034501 (22 July 2017). https://doi.org/10.1117/1.JMI.4.3.034501

Received: 10 February 2017; Accepted: 22 June 2017; Published: 22 July 2017

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