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Multi-scale segmentation and surface fitting for measuring 3D macular holes.

Nasrulloh, A. and Willcocks, C. and Jackson, P. and Geenen, C. and Habib, M. and Steel, D. and Obara, B. (2018) 'Multi-scale segmentation and surface fitting for measuring 3D macular holes.', IEEE transactions on medical imaging., 37 (2). pp. 580-589.


Macular holes are blinding conditions where a hole develops in the central part of retina, resulting in reduced central vision. The prognosis and treatment options are related to a number of variables including the macular hole size and shape. High-resolution spectral domain optical coherence tomography (SD-OCT) allows precise imaging of the macular hole geometry in three dimensions, but the measurement of these by human observers is time consuming and prone to high inter- and intra-observer variability, being characteristically measured in 2D rather than 3D. We introduce several novel techniques to automatically retrieve accurate 3D measurements of the macular hole, including: surface area, base area, base diameter, top area, top diameter, height, and minimum diameter. Specifically, we introduce a multi-scale 3D level set segmentation approach based on a state-of-the-art level set method, and we introduce novel curvature-based cutting and 3D measurement procedures. The algorithm is fully automatic, and we validate our extracted measurements both qualitatively and quantitatively, where our results show the method to be robust across a variety of scenarios. Our automated processes are considered a significant contribution for clinical applications.

Item Type:Article
Full text:(AM) Accepted Manuscript
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Publisher statement:© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:21 October 2017
Date deposited:23 October 2017
Date of first online publication:30 October 2017
Date first made open access:23 October 2017

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