Search Results for author:
Found 7 papers, 2 papers with code
Speckle2Speckle: Unsupervised Learning of Ultrasound Speckle Filtering Without Clean Data
To enable this, we make use of realistic ultrasound simulation techniques that allow for instantiation of several independent speckle realizations that represent the exact same tissue, thus allowing for the application of image reconstruction techniques that work with pairs of differently corrupted data.
Fully Automatic Segmentation of 3D Brain Ultrasound: Learning from Coarse Annotations
Trained on the dataset alone, we report a Dice and Jaccard coefficient of $0. 45 \pm 0. 09$ and $0. 30 \pm 0. 07$ respectively, as well as an average distance of $0. 78 \pm 0. 36~mm$.
End-to-End Learning-Based Ultrasound Reconstruction
The proposed method displays both promising image reconstruction quality and acquisition frequency when integrated for live ultrasound scanning.
Data Augmentation with Manifold Exploring Geometric Transformations for Increased Performance and Robustness
Compared with traditional augmentation methods, and with images synthesized by Generative Adversarial Networks our method not only achieves state-of-the-art performance but also significantly improves the network's robustness.
Redefining Ultrasound Compounding: Computational Sonography
By providing a novel paradigm for the acquisition and reconstruction of tracked freehand 3D ultrasound, this work presents the concept of Computational Sonography (CS) to model the directionality of ultrasound information.
Initialize globally before acting locally: Enabling Landmark-free 3D US to MRI Registration
Registration of partial-view 3D US volumes with MRI data is influenced by initialization.
SUPRA: Open Source Software Defined Ultrasound Processing for Real-Time Applications
Including all processing stages of a usual ultrasound pipeline, the run-time analysis shows that it can be executed in 2D and 3D on consumer GPUs in real-time.
