7 research outputs found
OT-driven Multi-Domain Unsupervised Ultrasound Image Artifact Removal using a Single CNN
Ultrasound imaging (US) often suffers from distinct image artifacts from
various sources. Classic approaches for solving these problems are usually
model-based iterative approaches that have been developed specifically for each
type of artifact, which are often computationally intensive. Recently, deep
learning approaches have been proposed as computationally efficient and high
performance alternatives. Unfortunately, in the current deep learning
approaches, a dedicated neural network should be trained with matched training
data for each specific artifact type. This poses a fundamental limitation in
the practical use of deep learning for US, since large number of models should
be stored to deal with various US image artifacts. Inspired by the recent
success of multi-domain image transfer, here we propose a novel, unsupervised,
deep learning approach in which a single neural network can be used to deal
with different types of US artifacts simply by changing a mask vector that
switches between different target domains. Our algorithm is rigorously derived
using an optimal transport (OT) theory for cascaded probability measures.
Experimental results using phantom and in vivo data demonstrate that the
proposed method can generate high quality image by removing distinct artifacts,
which are comparable to those obtained by separately trained multiple neural
networks
Phase asymmetry guided adaptive fractional-order total variation and diffusion for feature-preserving ultrasound despeckling
It is essential for ultrasound despeckling to remove speckle noise while
simultaneously preserving edge features for accurate diagnosis and analysis in
many applications. To preserve real edges such as ramp edges and low contrast
edges, we first detect edges using a phase-based measure called phase asymmetry
(PAS), which can distinguish small differences in transition border regions and
varies from to , taking in ideal smooth regions and taking at
ideal step edges. We further propose three strategies to properly preserve
edges. First, in observing that fractional-order anisotropic diffusion (FAD)
filter has good performance in smooth regions while the fractional-order TV
(FTV) filter performs better at edges, we leverage the PAS metric to keep a
balance between FAD filter and FTV filter for achieving the best performance of
preserving ramp edges. Second, considering that the FAD filter fails to protect
low contrast edges by solely integrating gradient information into the
diffusion coefficient, we integrate the PAS metric into the diffusion
coefficient to properly preserve low contrast edges. Finally, different from
fixed fractional order diffusion filters neglecting the differences between
smooth regions and transition border regions, an adaptive fractional order is
implemented based on the PAS metric to enhance edges. The experimental results
show that our method outperforms other state-of-the-art ultrasound despeckling
filters in both speckle reduction and feature preservation
Switchable Deep Beamformer
Recent proposals of deep beamformers using deep neural networks have
attracted significant attention as computational efficient alternatives to
adaptive and compressive beamformers. Moreover, deep beamformers are versatile
in that image post-processing algorithms can be combined with the beamforming.
Unfortunately, in the current technology, a separate beamformer should be
trained and stored for each application, demanding significant scanner
resources. To address this problem, here we propose a {\em switchable} deep
beamformer that can produce various types of output such as DAS, speckle
removal, deconvolution, etc., using a single network with a simple switch. In
particular, the switch is implemented through Adaptive Instance Normalization
(AdaIN) layers, so that various output can be generated by merely changing the
AdaIN code. Experimental results using B-mode focused ultrasound confirm the
flexibility and efficacy of the proposed methods for various applications
A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising
Most of existing image denoising methods assume the corrupted noise to be
additive white Gaussian noise (AWGN). However, the realistic noise in
real-world noisy images is much more complex than AWGN, and is hard to be
modelled by simple analytical distributions. As a result, many state-of-the-art
denoising methods in literature become much less effective when applied to
real-world noisy images captured by CCD or CMOS cameras. In this paper, we
develop a trilateral weighted sparse coding (TWSC) scheme for robust real-world
image denoising. Specifically, we introduce three weight matrices into the data
and regularisation terms of the sparse coding framework to characterise the
statistics of realistic noise and image priors. TWSC can be reformulated as a
linear equality-constrained problem and can be solved by the alternating
direction method of multipliers. The existence and uniqueness of the solution
and convergence of the proposed algorithm are analysed. Extensive experiments
demonstrate that the proposed TWSC scheme outperforms state-of-the-art
denoising methods on removing realistic noise.Comment: Accepted to ECCV 2018. 17 pages, not including supplemental material.
Code will be published on https://github.com/csjunxu/TWSC-ECCV201
Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound
Automatic prostate segmentation in transrectal ultrasound (TRUS) images is of
essential importance for image-guided prostate interventions and treatment
planning. However, developing such automatic solutions remains very challenging
due to the missing/ambiguous boundary and inhomogeneous intensity distribution
of the prostate in TRUS, as well as the large variability in prostate shapes.
This paper develops a novel 3D deep neural network equipped with attention
modules for better prostate segmentation in TRUS by fully exploiting the
complementary information encoded in different layers of the convolutional
neural network (CNN). Our attention module utilizes the attention mechanism to
selectively leverage the multilevel features integrated from different layers
to refine the features at each individual layer, suppressing the non-prostate
noise at shallow layers of the CNN and increasing more prostate details into
features at deep layers. Experimental results on challenging 3D TRUS volumes
show that our method attains satisfactory segmentation performance. The
proposed attention mechanism is a general strategy to aggregate multi-level
deep features and has the potential to be used for other medical image
segmentation tasks. The code is publicly available at
https://github.com/wulalago/DAF3D.Comment: 11 pages, 10 figures, 2 tables. Accepted by IEEE transactions on
Medical Imagin
Pushing the Limit of Unsupervised Learning for Ultrasound Image Artifact Removal
Ultrasound (US) imaging is a fast and non-invasive imaging modality which is
widely used for real-time clinical imaging applications without concerning
about radiation hazard. Unfortunately, it often suffers from poor visual
quality from various origins, such as speckle noises, blurring, multi-line
acquisition (MLA), limited RF channels, small number of view angles for the
case of plane wave imaging, etc. Classical methods to deal with these problems
include image-domain signal processing approaches using various adaptive
filtering and model-based approaches. Recently, deep learning approaches have
been successfully used for ultrasound imaging field. However, one of the
limitations of these approaches is that paired high quality images for
supervised training are difficult to obtain in many practical applications. In
this paper, inspired by the recent theory of unsupervised learning using
optimal transport driven cycleGAN (OT-cycleGAN), we investigate applicability
of unsupervised deep learning for US artifact removal problems without matched
reference data. Experimental results for various tasks such as deconvolution,
speckle removal, limited data artifact removal, etc. confirmed that our
unsupervised learning method provides comparable results to supervised learning
for many practical applications
Low Rank Regularization: A Review
Low rank regularization, in essence, involves introducing a low rank or
approximately low rank assumption for matrix we aim to learn, which has
achieved great success in many fields including machine learning, data mining
and computer version. Over the last decade, much progress has been made in
theories and practical applications. Nevertheless, the intersection between
them is very slight. In order to construct a bridge between practical
applications and theoretical research, in this paper we provide a comprehensive
survey for low rank regularization. We first review several traditional machine
learning models using low rank regularization, and then show their (or their
variants) applications in solving practical issues, such as non-rigid structure
from motion and image denoising. Subsequently, we summarize the regularizers
and optimization methods that achieve great success in traditional machine
learning tasks but are rarely seen in solving practical issues. Finally, we
provide a discussion and comparison for some representative regularizers
including convex and non-convex relaxations. Extensive experimental results
demonstrate that non-convex regularizers can provide a large advantage over the
nuclear norm, the regularizer widely used in solving practical issues.Comment: 16 pages,4 figures,4 table