Unsupervised Denoising of Optical Coherence Tomography Images with Dual_Merged CycleWGAN

@article{Du2022UnsupervisedDO,
  title={Unsupervised Denoising of Optical Coherence Tomography Images with Dual\_Merged CycleWGAN},
  author={J. Du and Xujian Yang and Kecheng Jin and Xuanzheng Qi and Hu Chen},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.00698}
}
—Nosie is an important cause of low quality Optical coherence tomography (OCT) image. The neural network model based on Convolutional neural networks(CNNs) has demonstrated its excellent performance in image denoising. However, OCT image denoising still faces great challenges because many previous neural network algorithms required a large number of labeled data, which might cost much time or is expensive. Besides, these CNN-based algorithms need numerous parameters and good tuning techniques… 

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References

SHOWING 1-10 OF 28 REFERENCES

Speckle noise reduction in optical coherence tomography images based on edge-sensitive cGAN.

TLDR
An end-to-end framework for simultaneous speckle reduction and contrast enhancement for retinal OCT images based on the conditional generative adversarial network (cGAN), with edge loss function added to the final objective so that the model is sensitive to the edge-related details.

Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network

TLDR
This work combines the autoencoder, deconvolution network, and shortcut connections into the residual encoder–decoder convolutional neural network (RED-CNN) for low-dose CT imaging and achieves a competitive performance relative to the-state-of-art methods in both simulated and clinical cases.

U-Net: Convolutional Networks for Biomedical Image Segmentation

TLDR
It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.

Wavelet denoising of multiframe optical coherence tomography data

TLDR
A novel speckle noise reduction algorithm for OCT images that uses wavelet decompositions of the single frames for a local noise and structure estimation and observes only a minor sharpness decrease at a signal-to-noise gain.

Shared-hole graph search with adaptive constraints for 3D optic nerve head optical coherence tomography image segmentation.

TLDR
This work proposes a 3D method for ONH centered SD-OCT image segmentation, which is based on a modified graph search algorithm with a shared-hole and locally adaptive constraints, and shows that both the optic disc boundary and nine retinal surfaces can be accurately segmented in SD- OCT images.

Segmentation Guided Registration for 3D Spectral-Domain Optical Coherence Tomography Images

TLDR
A new segmentation guided approach is reported for registration of retinal OCT data, which models the 3D registration as a two-stage registration including x-y direction registration and z direction registration.

A Primal-Dual Algorithm for OCT Image Denoising

TLDR
The proposed algorithm reduced the speckle noise in OCT images effectively and improved the quality of the images and the wide applicability of the proposed algorithm was demonstrated on image segmentation, target recognition and motion estimation.

Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion

TLDR
This work clearly establishes the value of using a denoising criterion as a tractable unsupervised objective to guide the learning of useful higher level representations.

Deriving external forces via convolutional neural networks for biomedical image segmentation.

TLDR
This paper proposes using GVF as reference to train a convolutional neural network to derive an external force, which is integrated into the active contour models for curve evolution and achieves competitive performance for different tasks compared to the state-of-the-art algorithms.