Noise Conscious Training of Non Local Neural Network powered by Self
Attentive Spectral Normalized Markovian Patch GAN for Low Dose CT Denoising
- URL: http://arxiv.org/abs/2011.05684v1
- Date: Wed, 11 Nov 2020 10:44:52 GMT
- Title: Noise Conscious Training of Non Local Neural Network powered by Self
Attentive Spectral Normalized Markovian Patch GAN for Low Dose CT Denoising
- Authors: Sutanu Bera, Prabir Kumar Biswas
- Abstract summary: Deep learning-based technique has emerged as a dominant method for low dose CT(LDCT) denoising.
In this study, we propose a novel convolutional module as the first attempt to utilize neighborhood similarity of CT images for denoising tasks.
Next, we moved towards the problem of non-stationarity of CT noise and introduced a new noise aware mean square error loss for LDCT denoising.
- Score: 20.965610734723636
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The explosive rise of the use of Computer tomography (CT) imaging in medical
practice has heightened public concern over the patient's associated radiation
dose. However, reducing the radiation dose leads to increased noise and
artifacts, which adversely degrades the scan's interpretability. Consequently,
an advanced image reconstruction algorithm to improve the diagnostic
performance of low dose ct arose as the primary concern among the researchers,
which is challenging due to the ill-posedness of the problem. In recent times,
the deep learning-based technique has emerged as a dominant method for low dose
CT(LDCT) denoising. However, some common bottleneck still exists, which hinders
deep learning-based techniques from furnishing the best performance. In this
study, we attempted to mitigate these problems with three novel accretions.
First, we propose a novel convolutional module as the first attempt to utilize
neighborhood similarity of CT images for denoising tasks. Our proposed module
assisted in boosting the denoising by a significant margin. Next, we moved
towards the problem of non-stationarity of CT noise and introduced a new noise
aware mean square error loss for LDCT denoising. Moreover, the loss mentioned
above also assisted to alleviate the laborious effort required while training
CT denoising network using image patches. Lastly, we propose a novel
discriminator function for CT denoising tasks. The conventional vanilla
discriminator tends to overlook the fine structural details and focus on the
global agreement. Our proposed discriminator leverage self-attention and
pixel-wise GANs for restoring the diagnostic quality of LDCT images. Our method
validated on a publicly available dataset of the 2016 NIH-AAPM-Mayo Clinic Low
Dose CT Grand Challenge performed remarkably better than the existing state of
the art method.
Related papers
- WIA-LD2ND: Wavelet-based Image Alignment for Self-supervised Low-Dose CT Denoising [74.14134385961775]
We introduce a novel self-supervised CT image denoising method called WIA-LD2ND, only using NDCT data.
WIA-LD2ND comprises two modules: Wavelet-based Image Alignment (WIA) and Frequency-Aware Multi-scale Loss (FAM)
arXiv Detail & Related papers (2024-03-18T11:20:11Z) - Rotational Augmented Noise2Inverse for Low-dose Computed Tomography
Reconstruction [83.73429628413773]
Supervised deep learning methods have shown the ability to remove noise in images but require accurate ground truth.
We propose a novel self-supervised framework for LDCT, in which ground truth is not required for training the convolutional neural network (CNN)
Numerical and experimental results show that the reconstruction accuracy of N2I with sparse views is degrading while the proposed rotational augmented Noise2Inverse (RAN2I) method keeps better image quality over a different range of sampling angles.
arXiv Detail & Related papers (2023-12-19T22:40:51Z) - The role of noise in denoising models for anomaly detection in medical
images [62.0532151156057]
Pathological brain lesions exhibit diverse appearance in brain images.
Unsupervised anomaly detection approaches have been proposed using only normal data for training.
We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes.
arXiv Detail & Related papers (2023-01-19T21:39:38Z) - Self-supervised Physics-based Denoising for Computed Tomography [2.2758845733923687]
Computed Tomography (CT) imposes risk on the patients due to its inherent X-ray radiation.
Lowering the radiation dose reduces the health risks but leads to noisier measurements, which decreases the tissue contrast and causes artifacts in CT images.
Modern deep learning noise suppression methods alleviate the challenge but require low-noise-high-noise CT image pairs for training.
We introduce a new self-supervised approach for CT denoising Noise2NoiseTD-ANM that can be trained without the high-dose CT projection ground truth images.
arXiv Detail & Related papers (2022-11-01T20:58:50Z) - Zero-shot Blind Image Denoising via Implicit Neural Representations [77.79032012459243]
We propose an alternative denoising strategy that leverages the architectural inductive bias of implicit neural representations (INRs)
We show that our method outperforms existing zero-shot denoising methods under an extensive set of low-noise or real-noise scenarios.
arXiv Detail & Related papers (2022-04-05T12:46:36Z) - Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis [68.5287824124996]
We present a new type of discriminator, the segmentor, to accurately locate the lesions and improve the visual quality of pseudo-healthy images.
We apply the generated images into medical image enhancement and utilize the enhanced results to cope with the low contrast problem.
Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods.
arXiv Detail & Related papers (2022-03-29T08:41:17Z) - Unsupervised Denoising of Retinal OCT with Diffusion Probabilistic Model [0.2578242050187029]
We present a diffusion probabilistic model that is fully unsupervised to learn from noise instead of signal.
Our method can significantly improve the image quality with a simple working pipeline and a small amount of training data.
arXiv Detail & Related papers (2022-01-27T19:02:38Z) - DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based
Discriminators for Low-Dose CT Denoising [22.351540738281265]
Deep learning techniques have been introduced to improve the image quality of LDCT images through denoising.
This paper proposes a novel method, termed DU-GAN, which leverages U-Net based discriminators in the GANs framework to learn both global and local difference between the denoised and normal-dose images.
arXiv Detail & Related papers (2021-08-24T14:37:46Z) - Task-Oriented Low-Dose CT Image Denoising [11.278150927185994]
We introduce a novel Task-Oriented Denoising Network (TOD-Net) with a task-oriented loss leveraging knowledge from the downstream tasks.
The presented work may shed light on the future development of context-aware image denoising methods.
arXiv Detail & Related papers (2021-03-25T01:47:55Z) - No-reference denoising of low-dose CT projections [2.7716102039510564]
Low-dose computed tomography (LDCT) became a clear trend in radiology with an aspiration to refrain from delivering excessive X-ray radiation to the patients.
The reduction of the radiation dose decreases the risks to the patients but raises the noise level, affecting the quality of the images and their ultimate diagnostic value.
One mitigation option is to consider pairs of low-dose and high-dose CT projections to train a denoising model using deep learning algorithms; however, such pairs are rarely available in practice.
In this paper, we present a new self-supervised method for CT denoising. Unlike existing self-supervised
arXiv Detail & Related papers (2021-02-03T13:51:33Z) - Improving Blind Spot Denoising for Microscopy [73.94017852757413]
We present a novel way to improve the quality of self-supervised denoising.
We assume the clean image to be the result of a convolution with a point spread function (PSF) and explicitly include this operation at the end of our neural network.
arXiv Detail & Related papers (2020-08-19T13:06:24Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.