@inproceedings{183a51b538e341efbc0a54f1f800d42f,
title = "Y-Net: A Spatiospectral Dual-Encoder Network for Medical Image Segmentation",
abstract = "Automated segmentation of retinal optical coherence tomography (OCT) images has become an important recent direction in machine learning for medical applications. We hypothesize that the anatomic structure of layers and their high-frequency variation in OCT images make retinal OCT a fitting choice for extracting spectral domain features and combining them with spatial domain features. In this work, we present Y-Net, an architecture that combines the frequency domain features with the image domain to improve the segmentation performance of OCT images. The results of this work demonstrate that the introduction of two branches, one for spectral and one for spatial domain features, brings very significant improvement in fluid segmentation performance and allows outperformance as compared to the well-known U-Net model. Our improvement was 13 % on the fluid segmentation dice score and 1.9 % on the average dice score. Finally, removing selected frequency ranges in the spectral domain demonstrates the impact of these features on the fluid segmentation outperformance. Code: github.com/azadef/ynet",
keywords = "Frequency domain in OCT, OCT segmentation, U-Net",
author = "Azade Farshad and Yousef Yeganeh and Peter Gehlbach and Nassir Navab",
note = "Funding Information: Acknowledgement. We gratefully acknowledge the Munich Center for Machine Learning (MCML) with funding from the Bundesministerium f{\"u}r Bildung und Forschung (BMBF) under the project 01IS18036B. Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 ; Conference date: 18-09-2022 Through 22-09-2022",
year = "2022",
doi = "10.1007/978-3-031-16434-7_56",
language = "English (US)",
isbn = "9783031164330",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "582--592",
editor = "Linwei Wang and Qi Dou and Fletcher, {P. Thomas} and Stefanie Speidel and Shuo Li",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings",
address = "Germany",
}