ArticleIEEE transactions on ultrasonics, ferroelectrics, and frequency control2020
Deep Learning to Obtain Simultaneous Image and Segmentation Outputs From a Single Input of Raw Ultrasound Channel Data.
Article in IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Dendritic U-Net for robust vascular wall segmentation in ultrasound imaging.Journal of medical ultrasonics (2001) · 2026Article
- Comparative Assessment of Real-Time and Offline Short-Lag Spatial Coherence Imaging of Ultrasound Breast Masses.Ultrasound in medicine & biology · 2025Article
- Overfit detection method for deep neural networks trained to beamform ultrasound images.Ultrasonics · 2025Article
- FLEX: FLexible Transducer With External Tracking for Ultrasound Imaging With Patient-Specific Geometry Estimation.IEEE transactions on bio-medical engineering · 2024Article
- Detection of COVID-19 features in lung ultrasound images using deep neural networks.Communications medicine · 2024Article
- Feature Extraction of Ultrasound Radiofrequency Data for the Classification of the Peripheral Zone of Human Prostate.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- A Deep Learning Approach for Beamforming and Contrast Enhancement of Ultrasound Images in Monostatic Synthetic Aperture Imaging: A Proof-of-Concept.IEEE open journal of engineering in medicine and biology · 2024Article
- Spatial Coherence Approaches to Distinguish Suspicious Mass Contents in Fundamental and Harmonic Breast Ultrasound Images.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2024Article
- A Robust and Explainable Structure-Based Algorithm for Detecting the Organ Boundary From Ultrasound Multi-Datasets.Journal of digital imaging · 2023Article
- Coherence Metrics for Reader-Independent Differentiation of Cystic From Solid Breast Masses in Ultrasound Images.Ultrasound in medicine & biology · 2023Article
- ESTAN: Enhanced Small Tumor-Aware Network for Breast Ultrasound Image Segmentation.Healthcare (Basel, Switzerland) · 2022Article
- Training Ultrasound Image Classification Deep-Learning Algorithms for Pneumothorax Detection Using a Synthetic Tissue Phantom Apparatus.Journal of imaging · 2022Article
- Deep-fUS: A Deep Learning Platform for Functional Ultrasound Imaging of the Brain Using Sparse Data.IEEE transactions on medical imaging · 2022Article
- A Review of Deep Learning Applications in Lung Ultrasound Imaging of COVID-19 Patients.BME frontiers · 2022Article
- Training Deep Network Ultrasound Beamformers With Unlabeled In Vivo Data.IEEE transactions on medical imaging · 2022Article
- Deep Learning for Ultrasound Image Formation: CUBDL Evaluation Framework and Open Datasets.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2021Article
- Upstream Machine Learning in Radiology.Radiologic clinics of North America · 2021Review
- Deep Learning for Ultrasound Beamforming in Flexible Array Transducer.IEEE transactions on medical imaging · 2021Article
- Evaluating Input Domain and Model Selection for Deep Network Ultrasound Beamforming.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2021Article
- Deep Convolutional Neural Networks for Displacement Estimation in ARFI Imaging.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2021Article
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Authors and funding
5 authors.
Funding
Abstract
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 (DAS) 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 deemphasizing less important surrounding structures. DNNs trained with Field II simulations were tested with simulated, experimental phantom, and in vivo data sets 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 data sets achieved mean ± standard deviation Dice similarity coefficients of 0.92 ± 0.13, 0.92 ± 0.03, and 0.77 ± 0.07, respectively, and generalized contrast-to-noise ratios (gCNRs) 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 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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