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Found 8 papers, 1 papers with code
Delineating Bone Surfaces in B-Mode Images Constrained by Physics of Ultrasound Propagation
Bone surface delineation in ultrasound is of interest due to its potential in diagnosis, surgical planning, and post-operative follow-up in orthopedics, as well as the potential of using bones as anatomical landmarks in surgical navigation.
Active Learning for Segmentation Based on Bayesian Sample Queries
In an active learning framework of selecting informed samples for manual labeling, expert clinician time for manual annotation can be optimally utilized, enabling the establishment of large labeled datasets for machine learning.
SCATGAN for Reconstruction of Ultrasound Scatterers Using Generative Adversarial Networks
We demonstrate robust reconstruction results, invariant to US viewing and imaging settings such as imaging direction and center frequency.
Siamese Networks with Location Prior for Landmark Tracking in Liver Ultrasound Sequences
Image-guided radiation therapy can benefit from accurate motion tracking by ultrasound imaging, in order to minimize treatment margins and radiate moving anatomical targets, e. g., due to breathing.
Iterative Interaction Training for Segmentation Editing Networks
Automatic segmentation has great potential to facilitate morphological measurements while simultaneously increasing efficiency.
Generative Adversarial Networks for MR-CT Deformable Image Registration
Performance for the abdominal region was similar to that of CT-MRI NMI registration (77. 4 vs. 78. 8%) when using 3D synthesizing MRIs (12 slices) and medium sized receptive fields for the discriminator.
Active Learning for Segmentation by Optimizing Content Information for Maximal Entropy
Segmentation is essential for medical image analysis tasks such as intervention planning, therapy guidance, diagnosis, treatment decisions.
Temporal Interpolation via Motion Field Prediction
Temporal interpolation of navigator slices an be used to reduce the number of navigator acquisitions without degrading specificity in stacking.
