https://github.com/workingcoder/EDCNN
EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising
https://github.com/workingcoder/EDCNN
ct-denoising denoising edcnn edge-enhancement low-dose low-dose-ct low-dose-ct-denoising
Last synced: 6 months ago
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EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising
- Host: GitHub
- URL: https://github.com/workingcoder/EDCNN
- Owner: workingcoder
- License: apache-2.0
- Created: 2021-03-06T08:16:11.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2021-04-26T01:21:09.000Z (about 4 years ago)
- Last Synced: 2024-08-08T23:21:29.125Z (10 months ago)
- Topics: ct-denoising, denoising, edcnn, edge-enhancement, low-dose, low-dose-ct, low-dose-ct-denoising
- Language: Python
- Homepage:
- Size: 1.58 MB
- Stars: 141
- Watchers: 2
- Forks: 10
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising
By [Tengfei Liang](https://github.com/workingcoder), [Yi Jin](https://scholar.google.com/citations?user=NQAenU0AAAAJ&hl=en&oi=sra), [Yidong Li](https://scholar.google.com/citations?hl=en&user=3PagRQEAAAAJ), [Tao Wang](https://scholar.google.com/citations?user=F3C5oAcAAAAJ&hl=en&oi=sra).
This repository is an official implementation of the paper [EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising](https://arxiv.org/abs/2011.00139). [`ArXiv`](https://arxiv.org/abs/2011.00139) [`IEEEXplore`](https://ieeexplore.ieee.org/document/9320928)
*Notes:*
This repository provides [model and loss implementation code](./code), which can be easily integrated into the user's project.
## Introduction
EDCNN is a new end-to-end Low-Dose CT Denoiser. Designed as the FCN structure, it can effectively realize the low-dose CT image denoising in the way of post-processing. With the noval edge enhancement module, densely connection and compound loss, the model has a good performance in preserving details and suppressing noise in this denoising task. (For more details, please refer to [the original paper](https://arxiv.org/abs/2011.00139))
Fig. 1: Overall architecture of the proposed EDCNN model.
## Denoised results
For fairness, we choose the [REDCNN](https://arxiv.org/abs/1702.00288), [WGAN](https://arxiv.org/abs/1708.00961) and [CPCE](https://arxiv.org/abs/1802.05656) for comparison, because of their design of the single model, which is the same as our [EDCNN](https://arxiv.org/abs/2011.00139) model. All these models adopt the structure of convolutional neural networks.
Fig. 2: Comparison with existing Models on the AAPM-Mayo Dataset.
## AAPM-Mayo dataset
In the experiment of our study, we utilize the dataset of the [2016 NIH AAPM-Mayo Clinic Low-Dose CT Grand Challenge](https://www.aapm.org/grandchallenge/lowdosect/), which is used by current mainstream methods in the field of low-dose CT image denoising. It contains the paired normal-dose CT (NDCT) images and synthetic quarter-dose CT images (LDCT) with a size of 512x512 pixels, collected from 10 patients. So there are LDCT images for inputs of the model and NDCT images as targets, which can support the supervised training process. [`Download`](https://www.dropbox.com/sh/txplswleuxgrdue/AABD_1W7-SKdXuZD4myvC2Hqa?dl=0)
## Citing EDCNN
If you find EDCNN useful in your research, please consider citing:
```bibtex
@article{Liang_2020,
title={EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising},
ISBN={9781728144801},
url={http://dx.doi.org/10.1109/ICSP48669.2020.9320928},
DOI={10.1109/icsp48669.2020.9320928},
journal={2020 15th IEEE International Conference on Signal Processing (ICSP)},
publisher={IEEE},
author={Liang, Tengfei and Jin, Yi and Li, Yidong and Wang, Tao},
year={2020},
month={Dec}
}
```
## License
This repository is released under the Apache 2.0 license. Please see the [LICENSE](./LICENSE) file for more information.