Spectral U-Net: Enhancing Medical Image Segmentation via Spectral Decomposition

Yaopeng Peng; Milan Sonka; Danny Z. Chen

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arXiv · 2409.09216

Spectral U-Net: Enhancing Medical Image Segmentation via Spectral Decomposition

Yaopeng Peng·Milan Sonka·Danny Z. Chen

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Abstract

This paper introduces Spectral U-Net, a novel deep learning network based on spectral decomposition, by exploiting Dual Tree Complex Wavelet Transform (DTCWT) for down-sampling and inverse Dual Tree Complex Wavelet Transform (iDTCWT) for up-sampling. We devise the corresponding Wave-Block and iWave-Block, integrated into the U-Net architecture, aiming at mitigating information loss during down-sampling and enhancing detail reconstruction during up-sampling. In the encoder, we first decompose the feature map into high and low-frequency components using DTCWT, enabling down-sampling while mitigating information loss. In the decoder, we utilize iDTCWT to reconstruct higher-resolution feature maps from down-sampled features. Evaluations on the Retina Fluid, Brain Tumor, and Liver Tumor segmentation datasets with the nnU-Net framework demonstrate the superiority of the proposed Spectral U-Net.

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Yaopeng Peng, Milan Sonka, Danny Z. Chen. 2024-09-13. Spectral U-Net: Enhancing Medical Image Segmentation via Spectral Decomposition. https://arxiv.org/abs/2409.09216

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