613 research outputs found
Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network
Model based iterative reconstruction (MBIR) algorithms for low-dose X-ray CT
are computationally expensive. To address this problem, we recently proposed a
deep convolutional neural network (CNN) for low-dose X-ray CT and won the
second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the
texture were not fully recovered. To address this problem, here we propose a
novel framelet-based denoising algorithm using wavelet residual network which
synergistically combines the expressive power of deep learning and the
performance guarantee from the framelet-based denoising algorithms. The new
algorithms were inspired by the recent interpretation of the deep convolutional
neural network (CNN) as a cascaded convolution framelet signal representation.
Extensive experimental results confirm that the proposed networks have
significantly improved performance and preserves the detail texture of the
original images.Comment: This will appear in IEEE Transaction on Medical Imaging, a special
issue of Machine Learning for Image Reconstructio
Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems
Recently, deep learning approaches with various network architectures have
achieved significant performance improvement over existing iterative
reconstruction methods in various imaging problems. However, it is still
unclear why these deep learning architectures work for specific inverse
problems. To address these issues, here we show that the long-searched-for
missing link is the convolution framelets for representing a signal by
convolving local and non-local bases. The convolution framelets was originally
developed to generalize the theory of low-rank Hankel matrix approaches for
inverse problems, and this paper further extends the idea so that we can obtain
a deep neural network using multilayer convolution framelets with perfect
reconstruction (PR) under rectilinear linear unit nonlinearity (ReLU). Our
analysis also shows that the popular deep network components such as residual
block, redundant filter channels, and concatenated ReLU (CReLU) do indeed help
to achieve the PR, while the pooling and unpooling layers should be augmented
with high-pass branches to meet the PR condition. Moreover, by changing the
number of filter channels and bias, we can control the shrinkage behaviors of
the neural network. This discovery leads us to propose a novel theory for deep
convolutional framelets neural network. Using numerical experiments with
various inverse problems, we demonstrated that our deep convolution framelets
network shows consistent improvement over existing deep architectures.This
discovery suggests that the success of deep learning is not from a magical
power of a black-box, but rather comes from the power of a novel signal
representation using non-local basis combined with data-driven local basis,
which is indeed a natural extension of classical signal processing theory.Comment: This will appear in SIAM Journal on Imaging Science
Quadratic Autoencoder (Q-AE) for Low-dose CT Denoising
Inspired by complexity and diversity of biological neurons, our group
proposed quadratic neurons by replacing the inner product in current artificial
neurons with a quadratic operation on input data, thereby enhancing the
capability of an individual neuron. Along this direction, we are motivated to
evaluate the power of quadratic neurons in popular network architectures,
simulating human-like learning in the form of quadratic-neuron-based deep
learning. Our prior theoretical studies have shown important merits of
quadratic neurons and networks in representation, efficiency, and
interpretability. In this paper, we use quadratic neurons to construct an
encoder-decoder structure, referred as the quadratic autoencoder, and apply it
to low-dose CT denoising. The experimental results on the Mayo low-dose CT
dataset demonstrate the utility of quadratic autoencoder in terms of image
denoising and model efficiency. To our best knowledge, this is the first time
that the deep learning approach is implemented with a new type of neurons and
demonstrates a significant potential in the medical imaging field
DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification
In this work, we present a fully automated lung computed tomography (CT)
cancer diagnosis system, DeepLung. DeepLung consists of two components, nodule
detection (identifying the locations of candidate nodules) and classification
(classifying candidate nodules into benign or malignant). Considering the 3D
nature of lung CT data and the compactness of dual path networks (DPN), two
deep 3D DPN are designed for nodule detection and classification respectively.
Specifically, a 3D Faster Regions with Convolutional Neural Net (R-CNN) is
designed for nodule detection with 3D dual path blocks and a U-net-like
encoder-decoder structure to effectively learn nodule features. For nodule
classification, gradient boosting machine (GBM) with 3D dual path network
features is proposed. The nodule classification subnetwork was validated on a
public dataset from LIDC-IDRI, on which it achieved better performance than
state-of-the-art approaches and surpassed the performance of experienced
doctors based on image modality. Within the DeepLung system, candidate nodules
are detected first by the nodule detection subnetwork, and nodule diagnosis is
conducted by the classification subnetwork. Extensive experimental results
demonstrate that DeepLung has performance comparable to experienced doctors
both for the nodule-level and patient-level diagnosis on the LIDC-IDRI
dataset.\footnote{https://github.com/uci-cbcl/DeepLung.git}Comment: 9 pages, 8 figures, IEEE WACV conference. arXiv admin note:
substantial text overlap with arXiv:1709.0553
DeepLung: 3D Deep Convolutional Nets for Automated Pulmonary Nodule Detection and Classification
In this work, we present a fully automated lung CT cancer diagnosis system,
DeepLung. DeepLung contains two parts, nodule detection and classification.
Considering the 3D nature of lung CT data, two 3D networks are designed for the
nodule detection and classification respectively. Specifically, a 3D Faster
R-CNN is designed for nodule detection with a U-net-like encoder-decoder
structure to effectively learn nodule features. For nodule classification,
gradient boosting machine (GBM) with 3D dual path network (DPN) features is
proposed. The nodule classification subnetwork is validated on a public dataset
from LIDC-IDRI, on which it achieves better performance than state-of-the-art
approaches, and surpasses the average performance of four experienced doctors.
For the DeepLung system, candidate nodules are detected first by the nodule
detection subnetwork, and nodule diagnosis is conducted by the classification
subnetwork. Extensive experimental results demonstrate the DeepLung is
comparable to the experienced doctors both for the nodule-level and
patient-level diagnosis on the LIDC-IDRI dataset
Denoising of 3-D Magnetic Resonance Images Using a Residual Encoder-Decoder Wasserstein Generative Adversarial Network
Structure-preserved denoising of 3D magnetic resonance imaging (MRI) images
is a critical step in medical image analysis. Over the past few years, many
algorithms with impressive performances have been proposed. In this paper,
inspired by the idea of deep learning, we introduce an MRI denoising method
based on the residual encoder-decoder Wasserstein generative adversarial
network (RED-WGAN). Specifically, to explore the structure similarity between
neighboring slices, a 3D configuration is utilized as the basic processing
unit. Residual autoencoders combined with deconvolution operations are
introduced into the generator network. Furthermore, to alleviate the
oversmoothing shortcoming of the traditional mean squared error (MSE) loss
function, the perceptual similarity, which is implemented by calculating the
distances in the feature space extracted by a pretrained VGG-19 network, is
incorporated with the MSE and adversarial losses to form the new loss function.
Extensive experiments are implemented to assess the performance of the proposed
method. The experimental results show that the proposed RED-WGAN achieves
performance superior to several state-of-the-art methods in both simulated and
real clinical data. In particular, our method demonstrates powerful abilities
in both noise suppression and structure preservation.Comment: To appear on Medical Image Analysis. 29 pages, 15 figures, 7 table
Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT
X-ray computed tomography (CT) using sparse projection views is a recent
approach to reduce the radiation dose. However, due to the insufficient
projection views, an analytic reconstruction approach using the filtered back
projection (FBP) produces severe streaking artifacts. Recently, deep learning
approaches using large receptive field neural networks such as U-Net have
demonstrated impressive performance for sparse- view CT reconstruction.
However, theoretical justification is still lacking. Inspired by the recent
theory of deep convolutional framelets, the main goal of this paper is,
therefore, to reveal the limitation of U-Net and propose new multi-resolution
deep learning schemes. In particular, we show that the alternative U- Net
variants such as dual frame and the tight frame U-Nets satisfy the so-called
frame condition which make them better for effective recovery of high frequency
edges in sparse view- CT. Using extensive experiments with real patient data
set, we demonstrate that the new network architectures provide better
reconstruction performance.Comment: This will appear in IEEE Transaction on Medical Imaging, a special
issue of Machine Learning for Image Reconstructio
Can Deep Learning Outperform Modern Commercial CT Image Reconstruction Methods?
Commercial iterative reconstruction techniques on modern CT scanners target
radiation dose reduction but there are lingering concerns over their impact on
image appearance and low contrast detectability. Recently, machine learning,
especially deep learning, has been actively investigated for CT. Here we design
a novel neural network architecture for low-dose CT (LDCT) and compare it with
commercial iterative reconstruction methods used for standard of care CT. While
popular neural networks are trained for end-to-end mapping, driven by big data,
our novel neural network is intended for end-to-process mapping so that
intermediate image targets are obtained with the associated search gradients
along which the final image targets are gradually reached. This learned dynamic
process allows to include radiologists in the training loop to optimize the
LDCT denoising workflow in a task-specific fashion with the denoising depth as
a key parameter. Our progressive denoising network was trained with the Mayo
LDCT Challenge Dataset, and tested on images of the chest and abdominal regions
scanned on the CT scanners made by three leading CT vendors. The best deep
learning based reconstructions are systematically compared to the best
iterative reconstructions in a double-blinded reader study. It is found that
our deep learning approach performs either comparably or favorably in terms of
noise suppression and structural fidelity, and runs orders of magnitude faster
than the commercial iterative CT reconstruction algorithms.Comment: 17 pages, 7 figure
GCN-MIF: Graph Convolutional Network with Multi-Information Fusion for Low-dose CT Denoising
Being low-level radiation exposure and less harmful to health, low-dose
computed tomography (LDCT) has been widely adopted in the early screening of
lung cancer and COVID-19. LDCT images inevitably suffer from the degradation
problem caused by complex noises. It was reported that deep learning (DL)-based
LDCT denoising methods using convolutional neural network (CNN) achieved
impressive denoising performance. Although most existing DL-based methods
(e.g., encoder-decoder framework) can implicitly utilize non-local and
contextual information via downsampling operator and 3D CNN, the explicit
multi-information (i.e., local, non-local, and contextual) integration may not
be explored enough. To address this issue, we propose a novel graph
convolutional network-based LDCT denoising model, namely GCN-MIF, to explicitly
perform multi-information fusion for denoising purpose. Concretely, by
constructing intra- and inter-slice graph, the graph convolutional network is
introduced to leverage the non-local and contextual relationships among pixels.
The traditional CNN is adopted for the extraction of local information.
Finally, the proposed GCN-MIF model fuses all the extracted local, non-local,
and contextual information. Extensive experiments show the effectiveness of our
proposed GCN-MIF model by quantitative and visualized results. Furthermore, a
double-blind reader study on a public clinical dataset is also performed to
validate the usability of denoising results in terms of the structural
fidelity, the noise suppression, and the overall score. Models and code are
available at https://github.com/tonyckc/GCN-MIF_demo.Comment: Submitted to TMI with under revie
A Cascaded Convolutional Neural Network for X-ray Low-dose CT Image Denoising
Image denoising techniques are essential to reducing noise levels and
enhancing diagnosis reliability in low-dose computed tomography (CT). Machine
learning based denoising methods have shown great potential in removing the
complex and spatial-variant noises in CT images. However, some residue
artifacts would appear in the denoised image due to complexity of noises. A
cascaded training network was proposed in this work, where the trained CNN was
applied on the training dataset to initiate new trainings and remove artifacts
induced by denoising. A cascades of convolutional neural networks (CNN) were
built iteratively to achieve better performance with simple CNN structures.
Experiments were carried out on 2016 Low-dose CT Grand Challenge datasets to
evaluate the method's performance.Comment: 9 pages, 9 figure
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