This work was supported by the NIH under NEI grant R01-EY024655 (PI: J.L. Prince) and NINDS grant R01-NS082347 (PI: P.A. Calabresi). This work was also supported in part by NIH NIA R01 AG027161 and NSF Grant DMS 1624776.
Biomedical Optics Express
Ultrasound imaging -- Research, Tomography -- Methods
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.
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He, Y., Carass, A., Liu, Y., Jedynak, B. M., Solomon, S. D., Saidha, S., ... & Prince, J. L. (2019). Deep learning based topology guaranteed surface and MME segmentation of multiple sclerosis subjects from retinal OCT. Biomedical Optics Express, 10(10), 5042-5058.