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OCT Segmentation: Integrating Open Parametric Contour Model of the Retinal Layers and Shape Constraint to the Mumford-Shah Functional

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posted on 13.06.2019, 08:43 by J Duan, W Xie, RW Liu, C Tench, I Gottlob, F Proudlock, L Bai
In this paper, we propose a novel retinal layer boundary model for segmentation of optical coherence tomography (OCT) images. The retinal layer boundary model consists of 9 open parametric contours representing the 9 retinal layers in OCT images. An intensity-based Mumford-Shah (MS) variational functional is first defined to evolve the retinal layer boundary model to segment the 9 layers simultaneously. By making use of the normals of open parametric contours, we construct equal sized adjacent narrowbands that are divided by each contour. Regional information in each narrowband can thus be integrated into the MS energy functional such that its optimisation is robust against different initialisations. A statistical prior is also imposed on the shape of the segmented parametric contours for the functional. As such, by minimising the MS energy functional the parametric contours can be driven towards the true boundaries of retinal layers, while the similarity of the contours with respect to training OCT shapes is preserved. Experimental results on real OCT images demonstrate that the method is accurate and robust to low quality OCT images with low contrast and high-level speckle noise, and it outperforms the recent geodesic distance based method for segmenting 9 layers of the retina in OCT images.

History

Citation

Lecture Notes in Computer Science LNCS, 2018, 11167, pp. 178-188

Author affiliation

/Organisation/COLLEGE OF LIFE SCIENCES/Biological Sciences/Neuroscience, Psychology and Behaviour

Source

International Workshop, ShapeMI 2018 Held in Conjunction with MICCAI 2018 Granada, Spain

Version

AM (Accepted Manuscript)

Published in

Lecture Notes in Computer Science LNCS

Publisher

Springer Verlag (Germany)

issn

0302-9743

eissn

1611-3349

isbn

978-3-030-04747-4

Copyright date

2018

Available date

13/06/2019

Publisher version

https://link.springer.com/chapter/10.1007/978-3-030-04747-4_17

Book series

Lecture Notes in Computer Science book series (LNCS);11167

Language

en

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