606 research outputs found
Ultrasound segmentation using U-Net: learning from simulated data and testing on real data
Segmentation of ultrasound images is an essential task in both diagnosis and
image-guided interventions given the ease-of-use and low cost of this imaging
modality. As manual segmentation is tedious and time consuming, a growing body
of research has focused on the development of automatic segmentation
algorithms. Deep learning algorithms have shown remarkable achievements in this
regard; however, they need large training datasets. Unfortunately, preparing
large labeled datasets in ultrasound images is prohibitively difficult.
Therefore, in this study, we propose the use of simulated ultrasound (US)
images for training the U-Net deep learning segmentation architecture and test
on tissue-mimicking phantom data collected by an ultrasound machine. We
demonstrate that the trained architecture on the simulated data is
transferrable to real data, and therefore, simulated data can be considered as
an alternative training dataset when real datasets are not available. The
second contribution of this paper is that we train our U- Net network on
envelope and B-mode images of the simulated dataset, and test the trained
network on real envelope and B- mode images of phantom, respectively. We show
that test results are superior for the envelope data compared to B-mode image.Comment: Accepted in EMBC 201
Ultrasound Signal Processing: From Models to Deep Learning
Medical ultrasound imaging relies heavily on high-quality signal processing
algorithms to provide reliable and interpretable image reconstructions.
Hand-crafted reconstruction methods, often based on approximations of the
underlying measurement model, are useful in practice, but notoriously fall
behind in terms of image quality. More sophisticated solutions, based on
statistical modelling, careful parameter tuning, or through increased model
complexity, can be sensitive to different environments. Recently, deep learning
based methods have gained popularity, which are optimized in a data-driven
fashion. These model-agnostic methods often rely on generic model structures,
and require vast training data to converge to a robust solution. A relatively
new paradigm combines the power of the two: leveraging data-driven deep
learning, as well as exploiting domain knowledge. These model-based solutions
yield high robustness, and require less trainable parameters and training data
than conventional neural networks. In this work we provide an overview of these
methods from the recent literature, and discuss a wide variety of ultrasound
applications. We aim to inspire the reader to further research in this area,
and to address the opportunities within the field of ultrasound signal
processing. We conclude with a future perspective on these model-based deep
learning techniques for medical ultrasound applications
Deep Learning for Ultrasound Image Formation:CUBDL Evaluation Framework and Open Datasets
Deep learning for ultrasound image formation is rapidly garnering research support and attention, quickly rising as the latest frontier in ultrasound image formation, with much promise to balance both image quality and display speed. Despite this promise, one challenge with identifying optimal solutions is the absence of unified evaluation methods and datasets that are not specific to a single research group. This article introduces the largest known international database of ultrasound channel data and describes the associated evaluation methods that were initially developed for the challenge on ultrasound beamforming with deep learning (CUBDL), which was offered as a component of the 2020 IEEE International Ultrasonics Symposium. We summarize the challenge results and present qualitative and quantitative assessments using both the initially closed CUBDL evaluation test dataset (which was crowd-sourced from multiple groups around the world) and additional in vivo breast ultrasound data contributed after the challenge was completed. As an example quantitative assessment, single plane wave images from the CUBDL Task 1 dataset produced a mean generalized contrast-to-noise ratio (gCNR) of 0.67 and a mean lateral resolution of 0.42 mm when formed with delay-and-sum beamforming, compared with a mean gCNR as high as 0.81 and a mean lateral resolution as low as 0.32 mm when formed with networks submitted by the challenge winners. We also describe contributed CUBDL data that may be used for training of future networks. The compiled database includes a total of 576 image acquisition sequences. We additionally introduce a neural-network-based global sound speed estimator implementation that was necessary to fairly evaluate the results obtained with this international database. The integration of CUBDL evaluation methods, evaluation code, network weights from the challenge winners, and all datasets described herein are publicly available (visit https://cubdl.jhu.edu for details). </p
Deep Coherence Learning: An Unsupervised Deep Beamformer for High Quality Single Plane Wave Imaging in Medical Ultrasound
Plane wave imaging (PWI) in medical ultrasound is becoming an important
reconstruction method with high frame rates and new clinical applications.
Recently, single PWI based on deep learning (DL) has been studied to overcome
lowered frame rates of traditional PWI with multiple PW transmissions. However,
due to the lack of appropriate ground truth images, DL-based PWI still remains
challenging for performance improvements. To address this issue, in this paper,
we propose a new unsupervised learning approach, i.e., deep coherence learning
(DCL)-based DL beamformer (DL-DCL), for high-quality single PWI. In DL-DCL, the
DL network is trained to predict highly correlated signals with a unique loss
function from a set of PW data, and the trained DL model encourages
high-quality PWI from low-quality single PW data. In addition, the DL-DCL
framework based on complex baseband signals enables a universal beamformer. To
assess the performance of DL-DCL, simulation, phantom and in vivo studies were
conducted with public datasets, and it was compared with traditional
beamformers (i.e., DAS with 75-PWs and DMAS with 1-PW) and other DL-based
methods (i.e., supervised learning approach with 1-PW and generative
adversarial network (GAN) with 1-PW). From the experiments, the proposed DL-DCL
showed comparable results with DMAS with 1-PW and DAS with 75-PWs in spatial
resolution, and it outperformed all comparison methods in contrast resolution.
These results demonstrated that the proposed unsupervised learning approach can
address the inherent limitations of traditional PWIs based on DL, and it also
showed great potential in clinical settings with minimal artifacts
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