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
A. Farshad and Y. Yeganeh—Equal Contribution.
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Acknowledgement
We gratefully acknowledge the Munich Center for Machine Learning (MCML) with funding from the Bundesministerium für Bildung und Forschung (BMBF) under the project 01IS18036B.
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Farshad, A., Yeganeh, Y., Gehlbach, P., Navab, N. (2022). Y-Net: A Spatiospectral Dual-Encoder Network for Medical Image Segmentation. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13432. Springer, Cham. https://doi.org/10.1007/978-3-031-16434-7_56
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