A Deep Learning Based Alternative to Beamforming Ultrasound Images

Nair, Arun Asokan; Tran, Trac D.; Reiter, Austin; Bell, Muyinatu A. Lediju

doi:10.1109/icassp.2018.8461575

Cited by 70 publications

(41 citation statements)

References 15 publications

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“…In order to handle the large difference in size between the raw data input and the image data output, two convolutional layers with strides 2 and 3 were added. Compared to [3], where the raw data were resampled to a smaller size, we hypothesize that the strided convolutions adapt to the downsampling task more efficiently and with lower loss of information. Five fully connected layers with decreasing numbers of neurons at the end of the network summarize the information in the different channels.…”

Section: Methodsmentioning

confidence: 99%

“…By using the raw data instead of the beamformed data as the input, we give the network access to full measured information and the opportunity to learn a different way of beamforming. Similar approaches are pursued by Nair et al [3,4] but with a main focus on segmentation rather than on reconstruction. Furthermore, they used simu-lated raw data showing only one subject per frame, whereas we train and test our network on both diverse phantom and in vivo data.…”

Section: Introductionmentioning

confidence: 98%

“…Beamformed data were used in [2] to learn a better compounding for plane wave imaging. Using raw radiofrequency channel data as input was proposed by Nair et al [3,4] for anechoic cyst segmentation. Using already processed data instead of raw data has some advantages.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning-based reconstruction of ultrasound images from raw channel data

et al. 2020

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Purpose We investigate the feasibility of reconstructing ultrasound images directly from raw channel data using a deep learning network. Starting from the raw data, we present the network the full measurement information, allowing for a more generic reconstruction to form, as compared to common reconstructions constrained by physical models using fixed speed of sound assumptions. Methods We propose a U-Net-like architecture for the given task. Additional layers with strided convolutions downsample the raw data. Hyperparameter optimization was used to find a suitable learning rate. We train and test our deep learning approach on plane wave ultrasound images with a single insonification angle. The dataset includes phantom as well as in vivo data. Results The images produced by our method are visually comparable to ones reconstructed with the conventional delay and sum algorithm. Deviations between prediction and ground truth are likely to be related to speckle noise. For the test set, the mean absolute error is 4.23 ± 1.52 for the phantom images and 6.09 ± 0.72 for the in vivo data. Conclusion The result shows the feasibility of our approach and opens up new research directions regarding information retrieval from raw channel data. As the networks reconstruction performance is limited by the quality of the ground truth images, using other ultrasound reconstruction technique or image types as target data would be of interest.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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Deep learning-based reconstruction of ultrasound images from raw channel data

et al. 2020

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“…Prior work from our group [25]- [27] introduced DNNs that were trained purely with simulated data to successfully extract information directly from raw radiofrequency (RF) single plane wave channel data, prior to the application of time delays or any other traditional beamforming steps. Similarly, Simpson et al [28] introduced a method to learn the entire beamforming process without applying delays to the input data.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning to Obtain Simultaneous Image and Segmentation Outputs From a Single Input of Raw Ultrasound Channel Data

Nair

Washington

Tran

et al. 2020

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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Single plane wave transmissions are promising for automated imaging tasks requiring high ultrasound frame rates over an extended field of view. However, a single plane wave insonification typically produces suboptimal image quality. To address this limitation, we are exploring the use of deep neural networks (DNNs) as an alternative to delay-and-sum beamforming. The objectives of this work are to obtain information directly from raw channel data and to simultaneously generate both a segmentation map for automated ultrasound tasks and a corresponding ultrasound B-mode image for interpretable supervision of the automation. We focus on visualizing and segmenting anechoic targets surrounded by tissue and ignoring or de-emphasizing less important surrounding structures. DNNs trained with Field II simulations were tested with simulated, experimental phantom, and in vivo datasets that were not included during training. With unfocused input channel data (i.e., prior to the application of receive time delays), simulated, experimental phantom, and in vivo test datasets achieved mean ± standard deviation Dice similarity coefficients of 0.92 ± 0.13, 0.92±0.03, and 0.77±0.07, respectively, and generalized contrastto-noise ratios (gCNR) of 0.95±0.08, 0.93±0.08, and 0.75±0.14, respectively. With subaperture beamformed channel data and a modification to the input layer of the DNN architecture to accept these data, the fidelity of image reconstruction increased (e.g., mean gCNR of multiple acquisitions of two in vivo breast cysts ranged 0.89-0.96), but DNN display frame rates were reduced from 395 Hz to 287 Hz. Overall, the DNNs successfully translated feature representations learned from simulated data to phantom and in vivo data, which is promising for this novel approach to simultaneous ultrasound image formation and segmentation.

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“…1 [4], is the most well-known architecture for biomedical image segmentation which takes advantages of several convolutional, max-pooling, and upsampling layers. The application of U-net on simulated US images has been recently proposed [6]. Second, in order to increase the size of data, various data augmentation strategies for medical images have been proposed [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Ultrasound segmentation using U-Net: learning from simulated data and testing on real data

Behboodi

Rivaz

2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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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 Bmode images of phantom, respectively. We show that test results are superior for the envelope data compared to B-mode image.

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A Deep Learning Based Alternative to Beamforming Ultrasound Images

Cited by 70 publications

References 15 publications

Deep learning-based reconstruction of ultrasound images from raw channel data

Deep learning-based reconstruction of ultrasound images from raw channel data

Deep Learning to Obtain Simultaneous Image and Segmentation Outputs From a Single Input of Raw Ultrasound Channel Data

Ultrasound segmentation using U-Net: learning from simulated data and testing on real data

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