19 research outputs found
Unsupervised Denoising for Satellite Imagery using Wavelet Subband CycleGAN
Multi-spectral satellite imaging sensors acquire various spectral band images
such as red (R), green (G), blue (B), near-infrared (N), etc. Thanks to the
unique spectroscopic property of each spectral band with respective to the
objects on the ground, multi-spectral satellite imagery can be used for various
geological survey applications. Unfortunately, image artifacts from imaging
sensor noises often affect the quality of scenes and have negative impacts on
the applications of satellite imagery. Recently, deep learning approaches have
been extensively explored for the removal of noises in satellite imagery. Most
deep learning denoising methods, however, follow a supervised learning scheme,
which requires matched noisy image and clean image pairs that are difficult to
collect in real situations. In this paper, we propose a novel unsupervised
multispectral denoising method for satellite imagery using wavelet subband
cycle-consistent adversarial network (WavCycleGAN). The proposed method is
based on unsupervised learning scheme using adversarial loss and
cycle-consistency loss to overcome the lack of paired data. Moreover, in
contrast to the standard image domain cycleGAN, we introduce a wavelet subband
domain learning scheme for effective denoising without sacrificing high
frequency components such as edges and detail information. Experimental results
for the removal of vertical stripe and wave noises in satellite imaging sensors
demonstrate that the proposed method effectively removes noises and preserves
important high frequency features of satellite images
Multi-Cycle-Consistent Adversarial Networks for CT Image Denoising
CT image denoising can be treated as an image-to-image translation task where
the goal is to learn the transform between a source domain (noisy images)
and a target domain (clean images). Recently, cycle-consistent adversarial
denoising network (CCADN) has achieved state-of-the-art results by enforcing
cycle-consistent loss without the need of paired training data. Our detailed
analysis of CCADN raises a number of interesting questions. For example, if the
noise is large leading to significant difference between domain and domain
, can we bridge and with an intermediate domain such that both
the denoising process between and and that between and are
easier to learn? As such intermediate domains lead to multiple cycles, how do
we best enforce cycle-consistency? Driven by these questions, we propose a
multi-cycle-consistent adversarial network (MCCAN) that builds intermediate
domains and enforces both local and global cycle-consistency. The global
cycle-consistency couples all generators together to model the whole denoising
process, while the local cycle-consistency imposes effective supervision on the
process between adjacent domains. Experiments show that both local and global
cycle-consistency are important for the success of MCCAN, which outperforms the
state-of-the-art.Comment: Accepted in ISBI 2020. 5 pages, 4 figure
Medical Image Generation using Generative Adversarial Networks
Generative adversarial networks (GANs) are unsupervised Deep Learning
approach in the computer vision community which has gained significant
attention from the last few years in identifying the internal structure of
multimodal medical imaging data. The adversarial network simultaneously
generates realistic medical images and corresponding annotations, which proven
to be useful in many cases such as image augmentation, image registration,
medical image generation, image reconstruction, and image-to-image translation.
These properties bring the attention of the researcher in the field of medical
image analysis and we are witness of rapid adaption in many novel and
traditional applications. This chapter provides state-of-the-art progress in
GANs-based clinical application in medical image generation, and cross-modality
synthesis. The various framework of GANs which gained popularity in the
interpretation of medical images, such as Deep Convolutional GAN (DCGAN),
Laplacian GAN (LAPGAN), pix2pix, CycleGAN, and unsupervised image-to-image
translation model (UNIT), continue to improve their performance by
incorporating additional hybrid architecture, has been discussed. Further, some
of the recent applications of these frameworks for image reconstruction, and
synthesis, and future research directions in the area have been covered.Comment: 19 pages, 3 figures, 5 table
Blind Deconvolution Microscopy Using Cycle Consistent CNN with Explicit PSF Layer
Deconvolution microscopy has been extensively used to improve the resolution
of the widefield fluorescent microscopy. Conventional approaches, which usually
require the point spread function (PSF) measurement or blind estimation, are
however computationally expensive. Recently, CNN based approaches have been
explored as a fast and high performance alternative. In this paper, we present
a novel unsupervised deep neural network for blind deconvolution based on cycle
consistency and PSF modeling layers. In contrast to the recent CNN approaches
for similar problem, the explicit PSF modeling layers improve the robustness of
the algorithm. Experimental results confirm the efficacy of the algorithm
Generative Adversarial Network in Medical Imaging: A Review
Generative adversarial networks have gained a lot of attention in the
computer vision community due to their capability of data generation without
explicitly modelling the probability density function. The adversarial loss
brought by the discriminator provides a clever way of incorporating unlabeled
samples into training and imposing higher order consistency. This has proven to
be useful in many cases, such as domain adaptation, data augmentation, and
image-to-image translation. These properties have attracted researchers in the
medical imaging community, and we have seen rapid adoption in many traditional
and novel applications, such as image reconstruction, segmentation, detection,
classification, and cross-modality synthesis. Based on our observations, this
trend will continue and we therefore conducted a review of recent advances in
medical imaging using the adversarial training scheme with the hope of
benefiting researchers interested in this technique.Comment: 24 pages; v4; added missing references from before Jan 1st 2019;
accepted to MedI
Deep Generative Adversarial Networks: Applications in Musculoskeletal Imaging
In recent years, deep learning techniques have been applied in musculoskeletal radiology to increase the diagnostic potential of acquired images. Generative adversarial networks (GANs), which are deep neural networks that can generate or transform images, have the potential to aid in faster imaging by generating images with a high level of realism across multiple contrast and modalities from existing imaging protocols. This review introduces the key architectures of GANs as well as their technical background and challenges. Key research trends are highlighted, including: (a) reconstruction of high-resolution MRI; (b) image synthesis with different modalities and contrasts; (c) image enhancement that efficiently preserves high-frequency information suitable for human interpretation; (d) pixel-level segmentation with annotation sharing between domains; and (e) applications to different musculoskeletal anatomies. In addition, an overview is provided of the key issues wherein clinical applicability is challenging to capture with conventional performance metrics and expert evaluation. When clinically validated, GANs have the potential to improve musculoskeletal imaging.ope
Constrained CycleGAN for Effective Generation of Ultrasound Sector Images of Improved Spatial Resolution
Objective. A phased or a curvilinear array produces ultrasound (US) images
with a sector field of view (FOV), which inherently exhibits spatially-varying
image resolution with inferior quality in the far zone and towards the two
sides azimuthally. Sector US images with improved spatial resolutions are
favorable for accurate quantitative analysis of large and dynamic organs, such
as the heart. Therefore, this study aims to translate US images with
spatially-varying resolution to ones with less spatially-varying resolution.
CycleGAN has been a prominent choice for unpaired medical image translation;
however, it neither guarantees structural consistency nor preserves
backscattering patterns between input and generated images for unpaired US
images. Approach. To circumvent this limitation, we propose a constrained
CycleGAN (CCycleGAN), which directly performs US image generation with unpaired
images acquired by different ultrasound array probes. In addition to
conventional adversarial and cycle-consistency losses of CycleGAN, CCycleGAN
introduces an identical loss and a correlation coefficient loss based on
intrinsic US backscattered signal properties to constrain structural
consistency and backscattering patterns, respectively. Instead of
post-processed B-mode images, CCycleGAN uses envelope data directly obtained
from beamformed radio-frequency signals without any other non-linear
postprocessing. Main Results. In vitro phantom results demonstrate that
CCycleGAN successfully generates images with improved spatial resolution as
well as higher peak signal-to-noise ratio (PSNR) and structural similarity
(SSIM) compared with benchmarks. Significance. CCycleGAN-generated US images of
the in vivo human beating heart further facilitate higher quality heart wall
motion estimation than benchmarks-generated ones, particularly in deep regions
Self-supervised Dynamic CT Perfusion Image Denoising with Deep Neural Networks
Dynamic computed tomography perfusion (CTP) imaging is a promising approach
for acute ischemic stroke diagnosis and evaluation. Hemodynamic parametric maps
of cerebral parenchyma are calculated from repeated CT scans of the first pass
of iodinated contrast through the brain. It is necessary to reduce the dose of
CTP for routine applications due to the high radiation exposure from the
repeated scans, where image denoising is necessary to achieve a reliable
diagnosis. In this paper, we proposed a self-supervised deep learning method
for CTP denoising, which did not require any high-dose reference images for
training. The network was trained by mapping each frame of CTP to an estimation
from its adjacent frames. Because the noise in the source and target was
independent, this approach could effectively remove the noise. Being free from
high-dose training images granted the proposed method easier adaptation to
different scanning protocols. The method was validated on both simulation and a
public real dataset. The proposed method achieved improved image quality
compared to conventional denoising methods. On the real data, the proposed
method also had improved spatial resolution and contrast-to-noise ratio
compared to supervised learning which was trained on the simulation dataComment: 13 pages, 9 figure
CycleGAN with a Blur Kernel for Deconvolution Microscopy: Optimal Transport Geometry
Deconvolution microscopy has been extensively used to improve the resolution
of the wide-field fluorescent microscopy, but the performance of classical
approaches critically depends on the accuracy of a model and optimization
algorithms. Recently, the convolutional neural network (CNN) approaches have
been studied as a fast and high performance alternative. Unfortunately, the CNN
approaches usually require matched high resolution images for supervised
training. In this paper, we present a novel unsupervised cycle-consistent
generative adversarial network (cycleGAN) with a linear blur kernel, which can
be used for both blind- and non-blind image deconvolution. In contrast to the
conventional cycleGAN approaches that require two deep generators, the proposed
cycleGAN approach needs only a single deep generator and a linear blur kernel,
which significantly improves the robustness and efficiency of network training.
We show that the proposed architecture is indeed a dual formulation of an
optimal transport problem that uses a special form of the penalized least
squares cost as a transport cost. Experimental results using simulated and real
experimental data confirm the efficacy of the algorithm.Comment: This paper is accepted for IEEE Trans. Computational Imagin
Unpaired Deep Learning for Accelerated MRI using Optimal Transport Driven CycleGAN
Recently, deep learning approaches for accelerated MRI have been extensively
studied thanks to their high performance reconstruction in spite of
significantly reduced runtime complexity. These neural networks are usually
trained in a supervised manner, so matched pairs of subsampled and fully
sampled k-space data are required. Unfortunately, it is often difficult to
acquire matched fully sampled k-space data, since the acquisition of fully
sampled k-space data requires long scan time and often leads to the change of
the acquisition protocol. Therefore, unpaired deep learning without matched
label data has become a very important research topic. In this paper, we
propose an unpaired deep learning approach using a optimal transport driven
cycle-consistent generative adversarial network (OT-cycleGAN) that employs a
single pair of generator and discriminator. The proposed OT-cycleGAN
architecture is rigorously derived from a dual formulation of the optimal
transport formulation using a specially designed penalized least squares cost.
The experimental results show that our method can reconstruct high resolution
MR images from accelerated k- space data from both single and multiple coil
acquisition, without requiring matched reference data.Comment: Accepted for IEEE Transactions on Computational Imagin