Y-Net: A Spatiospectral Dual-Encoder Network for Medical Image Segmentation

Farshad A, Yeganeh Y, Gehlbach P, Navab N (2022)

Publication Type: Conference contribution

Publication year: 2022

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 13432 LNCS

Pages Range: 582-592

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Singapore, SGP

ISBN: 9783031164330

DOI: 10.1007/978-3-031-16434-7_56

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

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) Johns Hopkins University (JHU) US United States (USA) (US)

How to cite

APA:

Farshad, A., Yeganeh, Y., Gehlbach, P., & Navab, N. (2022). Y-Net: A Spatiospectral Dual-Encoder Network for Medical Image Segmentation. In Linwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 582-592). Singapore, SGP: Springer Science and Business Media Deutschland GmbH.

MLA:

Farshad, Azade, et al. "Y-Net: A Spatiospectral Dual-Encoder Network for Medical Image Segmentation." Proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, Singapore, SGP Ed. Linwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li, Springer Science and Business Media Deutschland GmbH, 2022. 582-592.

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