J-Invariant Volume Shuffle for Self-Supervised Cryo-Electron Tomogram Denoising on Single Noisy Volume

• URL: http://arxiv.org/abs/2411.15248v3
• Date: Sat, 22 Feb 2025 13:20:54 GMT
• Title: J-Invariant Volume Shuffle for Self-Supervised Cryo-Electron Tomogram Denoising on Single Noisy Volume
• Authors: Xiwei Liu, Mohamad Kassab, Min Xu, Qirong Ho,
• Abstract summary: Cryo-Electron Tomography (Cryo-ET) enables detailed 3D visualization of cellular structures in near-native states.<n>Cryo-ET suffers from low signal-to-noise ratio due to imaging constraints.<n>We propose a novel self-supervised learning model that denoises Cryo-ET volumetric images using a single noisy volume.
• Score: 11.183171651157892
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cryo-Electron Tomography (Cryo-ET) enables detailed 3D visualization of cellular structures in near-native states but suffers from low signal-to-noise ratio due to imaging constraints. Traditional denoising methods and supervised learning approaches often struggle with complex noise patterns and the lack of paired datasets. Self-supervised methods, which utilize noisy input itself as a target, have been studied; however, existing Cryo-ET self-supervised denoising methods face significant challenges due to losing information during training and the learned incomplete noise patterns. In this paper, we propose a novel self-supervised learning model that denoises Cryo-ET volumetric images using a single noisy volume. Our method features a U-shape J-invariant blind spot network with sparse centrally masked convolutions, dilated channel attention blocks, and volume unshuffle/shuffle technique. The volume-unshuffle/shuffle technique expands receptive fields and utilizes multi-scale representations, significantly improving noise reduction and structural preservation. Experimental results demonstrate that our approach achieves superior performance compared to existing methods, advancing Cryo-ET data processing for structural biology research

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