SC-Net

Here is an official implementation for paper "Self-Supervised Cryo-Electron Tomography Volumetric Image Restoration from Single Noisy Volume with Sparsity Constraint" accepted in ICCV 2021.

Introduction

Cryo-Electron Tomography (cryo-ET) is a powerful tool for 3D cellular visualization. Due to instrumental limitations, cryo-ET images and their volumetric reconstruction suffer from extremely low signal-to-noise ratio. In this paper, we propose a novel end-to-end self-supervised learning model, the Sparsity Constrained Network (SC-Net), to restore volumetric image from single noisy data in cryo-ET. The proposed method only requires a single noisy data as training input and no ground-truth is needed in the whole training procedure. A new target function is proposed to preserve both local smoothness and detailed structure. Additionally, a novel procedure for the simulation of electron tomographic photographing is designed to help the evaluation of methods. Experiments are done on three simulated data and four real-world data. The results show that our method could produce a strong enhancement for a single very noisy cryo-ET volumetric data, which is much better than the state-of-the-art Noise2Void, and with a competitive performance comparing with Noise2Noise.

Operation System

Ubuntu 18.04 or CentOS7

Requirements

Python 3.6.13
Pytorch 1.7.1
opencv-python 4.5.1
numpy 1.19.2
scikit-image 0.17.1
scikit-learn 0.24.2
mrcfile 1.3.0
topaz-em 0.2.4
numba 0.51.2

Pretrained Model and Dataset

Pretrained models for real-world datasets:
https://drive.google.com/file/d/18yaCdxlLbNU_eg1cIkgFIE9LpaxiF83E/view?usp=sharing
Real-World Training dataset:
https://drive.google.com/file/d/13wd6mlUqA47elQsOeFY8BmHN24hkffRe/view?usp=sharing

Usage

Training data directory is advised to build as follow

DATASET_NAME
-- test
-- train_noisy
-- train_prior
-- val_noisy
-- val_prior

Run training/testing script

sh run.sh

For detailed parameter settings, please run

python main.py --help

Acknowledgement

We sinceresly thank following work with their open-sourced code.
Bepler, T., Kelley, K., Noble, A.J., Berger, B. Topaz-Denoise: general deep denoising models for cryoEM and cryoET. Nat Commun 11, 5208 (2020). https://doi.org/10.1038/s41467-020-18952-1
Alexander Krull, Tim-Oliver Buchholz, Florian Jug. Noise2Void - Learning Denoising From Single Noisy Images. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 2129-2137. https://arxiv.org/abs/1811.10980

Citation

@InProceedings{Yang_2021_ICCV,
    author    = {Yang, Zhidong and Zhang, Fa and Han, Renmin},
    title     = {Self-Supervised Cryo-Electron Tomography Volumetric Image Restoration From Single Noisy Volume With Sparsity Constraint},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {4056-4065}
}

icthrm / SC-Net