publication . Preprint . 2018

WaveFlow - Towards Integration of Ultrasound Processing with Deep Learning

Jarosik, Piotr; Byra, Michał; Lewandowski, Marcin;
Open Access English
  • Published: 05 Nov 2018
Abstract
The ultimate goal of this work is a real-time processing framework for ultrasound image reconstruction augmented with machine learning. To attain this, we have implemented WaveFlow - a set of ultrasound data acquisition and processing tools for TensorFlow. WaveFlow includes: ultrasound Environments (connection points between the input raw ultrasound data source and TensorFlow) and signal processing Operators (ops) library. Raw data can be processed in real-time using algorithms available both in TensorFlow and WaveFlow. Currently, WaveFlow provides ops for B-mode image reconstruction (beamforming), signal processing and quantitative ultrasound. The ops were impl...
Subjects
acm: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Electrical Engineering and Systems Science - Signal Processing
Download from

[1] https://github.com/waveflow-team/waveflow/

[2] https://www.tensorflow.org/

[3] https://brain.fuw.edu.pl/edu/index.php/USG

[4] Byra Michał, et al. ”Combining Nakagami imaging and convolutional neural network for breast lesion classification.” Ultrasonics Symposium (IUS), 2017 IEEE International. IEEE, 2017.

[5] Smistad Erik, Østvik Andreas. ”2D left ventricle segmentation using deep learning.”, Ultrasonics Symposium (IUS), 2017 IEEE International. IEEE, 2017

[6] Perdios Dimitris, et al. ”A deep learning approach to ultrasound image recovery.” Ultrasonics Symposium (IUS), 2017 IEEE International. IEEE, 2017. [OpenAIRE]

[7] Lewandowski Marcin. ”Ultrasound Medical Imaging in the GPU Era.” GPU Technology Conference, San Diego, 2018.

[8] Lewandowski Marcin, et al. ”Modular & scalable ultrasound platform with GPU processing.” Ultrasonics Symposium (IUS), 2012 IEEE International. IEEE, 2012.

[9] Hyun Dongwoon. ”Customizable Ultrasound Imaging in Real-Time Using a GPU-Accelerated Beamformer.” GPU Technology Conference, San Diego, 2018.

[10] Szepesvari Csaba. ”Algorithms for reinforcement learning.” Synthesis lectures on artificial intelligence and machine learning 4.1 (2010): 1- 103.

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Abstract
The ultimate goal of this work is a real-time processing framework for ultrasound image reconstruction augmented with machine learning. To attain this, we have implemented WaveFlow - a set of ultrasound data acquisition and processing tools for TensorFlow. WaveFlow includes: ultrasound Environments (connection points between the input raw ultrasound data source and TensorFlow) and signal processing Operators (ops) library. Raw data can be processed in real-time using algorithms available both in TensorFlow and WaveFlow. Currently, WaveFlow provides ops for B-mode image reconstruction (beamforming), signal processing and quantitative ultrasound. The ops were impl...
Subjects
acm: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Electrical Engineering and Systems Science - Signal Processing
Download from

[1] https://github.com/waveflow-team/waveflow/

[2] https://www.tensorflow.org/

[3] https://brain.fuw.edu.pl/edu/index.php/USG

[4] Byra Michał, et al. ”Combining Nakagami imaging and convolutional neural network for breast lesion classification.” Ultrasonics Symposium (IUS), 2017 IEEE International. IEEE, 2017.

[5] Smistad Erik, Østvik Andreas. ”2D left ventricle segmentation using deep learning.”, Ultrasonics Symposium (IUS), 2017 IEEE International. IEEE, 2017

[6] Perdios Dimitris, et al. ”A deep learning approach to ultrasound image recovery.” Ultrasonics Symposium (IUS), 2017 IEEE International. IEEE, 2017. [OpenAIRE]

[7] Lewandowski Marcin. ”Ultrasound Medical Imaging in the GPU Era.” GPU Technology Conference, San Diego, 2018.

[8] Lewandowski Marcin, et al. ”Modular & scalable ultrasound platform with GPU processing.” Ultrasonics Symposium (IUS), 2012 IEEE International. IEEE, 2012.

[9] Hyun Dongwoon. ”Customizable Ultrasound Imaging in Real-Time Using a GPU-Accelerated Beamformer.” GPU Technology Conference, San Diego, 2018.

[10] Szepesvari Csaba. ”Algorithms for reinforcement learning.” Synthesis lectures on artificial intelligence and machine learning 4.1 (2010): 1- 103.

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