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Biomed Opt Express. 2019 Sep 12;10(10):5042-5058. doi: 10.1364/BOE.10.005042. eCollection 2019 Oct 1.

Deep learning based topology guaranteed surface and MME segmentation of multiple sclerosis subjects from retinal OCT.

Author information

1
Deptartment of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.
2
Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA.
3
Department of Mathematics & Statistics, Portland State University, Portland, OR 97201, USA.
4
Wilmer Eye Institute, The Johns Hopkins University School of Medicine, MD 21287, USA.
5
Department of Neurology, The Johns Hopkins University School of Medicine, MD 21287, USA.

Abstract

Optical coherence tomography (OCT) is a noninvasive imaging modality that can be used to obtain depth images of the retina. Patients with multiple sclerosis (MS) have thinning retinal nerve fiber and ganglion cell layers, and approximately 5% of MS patients will develop microcystic macular edema (MME) within the retina. Segmentation of both the retinal layers and MME can provide important information to help monitor MS progression. Graph-based segmentation with machine learning preprocessing is the leading method for retinal layer segmentation, providing accurate surface delineations with the correct topological ordering. However, graph methods are time-consuming and they do not optimally incorporate joint MME segmentation. This paper presents a deep network that extracts continuous, smooth, and topology-guaranteed surfaces and MMEs. The network learns shape priors automatically during training rather than being hard-coded as in graph methods. In this new approach, retinal surfaces and MMEs are segmented together with two cascaded deep networks in a single feed forward propagation. The proposed framework obtains retinal surfaces (separating the layers) with sub-pixel surface accuracy comparable to the best existing graph methods and MMEs with better accuracy than the state-of-the-art method. The full segmentation operation takes only ten seconds for a 3D volume.

Conflict of interest statement

The authors declare that there are no conflicts of interest related to this article.

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