@inproceedings{9cf90e04a4f148f195f333e0e0e9baff,
title = "Retinal Layer Segmentation from Oct Images Using 2D-3D Hybrid Network with Multi-Scale Loss and Refinement Module",
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.",
keywords = "CNN, optical coherence tomography, retinal layer, segmentation",
author = "Tsubasa Konno and Takahiro Ninomiya and Kanta Miura and Koichi Ito and Noriko Himori and Parmanand Sharma and Toru Nakazawa and Takafumi Aoki",
note = "Funding Information: This work was supported in part by JSPS KAKENHI Grant Numbers 21H03457. Publisher Copyright: {\textcopyright} 2023 IEEE.; 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 ; Conference date: 18-04-2023 Through 21-04-2023",
year = "2023",
doi = "10.1109/ISBI53787.2023.10230693",
language = "English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
booktitle = "2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023",
address = "United States",
}