Retinal layer segmentation from OCT images using 2D-3D hybrid network with multi-scale loss and refinement module
Tsubasa Konno (Tohoku University), Takahiro Ninomiya (Tohoku University), Kanta Miura (Tohoku University), Koichi Ito (Tohoku University), Noriko Himori (Tohoku University), Parmanand Sharma (Tohoku University), Toru Nakazawa (Tohoku University), Takafumi Aoki (Tohoku University)
International Symposium on Biomedical Imaging, pp. 1--5, April 2023.
Abstract
We propose a method of segmenting retinal layers from optical coherence tomography (OCT) images for the diagnosis. The proposed method estimates the pixel-wise labels of each retinal layer and each layer surface position using convolutional neural network (CNN). We introduce CNN to a multi-scale loss and a refinement module to improve the accuracy of pixel-wise labels and layer surface position. Through experiments using a public OCT image dataset, we demonstrate that the proposed method exhibits higher accuracy of segmenting retinal layers than the state-of-the-art methods.