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

• Published in: Biomedical optics express Vol. 10; no. 10; pp. 5042 - 5058
• Main Authors: He, Yufan, Carass, Aaron, Liu, Yihao, Jedynak, Bruno M., Solomon, Sharon D., Saidha, Shiv, Calabresi, Peter A., Prince, Jerry L.
• Format: Journal Article
• Language: English
• Published: United States Optical Society of America 01.10.2019
• DOI: 10.1364/BOE.10.005042
• ISSN: 2156-7085

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.