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.
Highlights
Optical coherence tomography (OCT) is a widely used non-invasive and non-ionizing modality which can obtain 3D retinal images rapidly [1]
The retinal depth information obtained from OCT enables measurements of layer thicknesses, which are known to change with certain diseases [2]
We present a deep learning framework for microcystic macular edema (MME) and topology-guaranteed retinal surfaces segmentation
Summary
Optical coherence tomography (OCT) is a widely used non-invasive and non-ionizing modality which can obtain 3D retinal images rapidly [1]. Ravishankar et al [30] and He et al [25] used a second auto-encoder network to learn the segmentation shape prior These methods can improve the shape and topology of the segmentation results, but none guarantees the output to provide a correct ordering of the segmented layers due to the pixel-wise labeling nature of FCNs. In this paper, we present a deep learning framework for MME and topology-guaranteed retinal surfaces segmentation. By decoupling the training of S-Net and R-Net, it is much easier to build data augmentation methods for training R-Net, as noted in [30] Another benefit of our approach over the direct regression method of Shah et al [32], is that we obtain MME pseudocyst masks directly from S-Net. Our approach offers three distinct advantages over the previous publicly available state-of-the-art graph based methods [18]: 1) it is an order of magnitude faster; 2) it provides both layer and MME segmentation; and 3) it is the first deep learning approach to guarantee the correct layer segmentation topology and output surface positions directly
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