Retinal Layer Segmentation Reformulated as OCT Language Processing

In the medical field, semantic segmentation has recently been dominated by deep-learning based image processing methods. Convolutional Neural Network approaches analyze image patches, draw complex features and latent representations and take advantage of these to label image pixels and voxels. In this paper, we investigate the usefulness of Recurrent Neural Network (RNN) for segmentation of OCT images, in which the intensity of elements of each A-mode depend on the path projected light takes through anatomical tissues to reach that point. The idea of this work is to reformulate this sequential voxel labeling/segmentation problem as language processing. Instead of treating images as patches, we regard them as a set of pixel column sequences and thus tackle the task of image segmentation, in this case pixel sequence labeling, as a natural language processing alike problem. Anatomical consistency, i.e. expected sequence of voxels representing retinal layers of eye’s anatomy along each OCT ray, serves as a fixed and learnable grammar. We show the effectiveness of this approach on a layer segmentation task for retinal Optical Coherence Tomography (OCT) data. Due to the inherent directionality of the modality, certain properties and artifacts such as varying signal strength and shadowing form a consistent pattern along increasing imaging depth. The retinal layer structure lends itself to our approach due to the fixed order of layers along the imaging direction. We investigate the influence of different model choices including simple RNNS, LSTMs and GRU structures on the outcome of this layer segmentation approach. Experimental results show that the potential of this idea that is on par with state of the art works while being flexible to changes in the data structure.