東北大学 大学院情報科学研究科 情報基礎科学専攻 計算機構論分野
(東北大学 工学部 電気情報物理工学科 情報工学コース)
青木・伊藤(康)研究室

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.

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