Open AccessDeep learning based topology guaranteed surface and MME segmentation of multiple sclerosis subjects from retinal OCT
Author(s) -
Yufan He,
Aaron Carass,
Yihao Liu,
Bruno Jedynak,
Sharon D. Solomon,
Shiv Saidha,
Peter A. Calabresi,
Jerry L. Prince
Publication year - 2019
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.10.005042
Subject(s) - computer science , segmentation , optical coherence tomography , artificial intelligence , retina , preprocessor , image segmentation , computer vision , inner plexiform layer , pattern recognition (psychology) , retinal , graph , cut , optics , ophthalmology , theoretical computer science , physics , medicine
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.