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Found 6 papers, 1 papers with code
PRO-TIP: Phantom for RObust automatic ultrasound calibration by TIP detection
We propose a novel method to automatically calibrate tracked ultrasound probes.
Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient
It is often unclear how to optimize abstract metrics, such as human expert perception, in convolutional neural network (CNN) training.
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 $0. 78 \pm 0. 36~mm$.
Weakly-Supervised White and Grey Matter Segmentation in 3D Brain Ultrasound
We implement label transfer from MRI to US, which is prone to a residual but inevitable registration error.
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.
Markerless Inside-Out Tracking for Interventional Applications
Tracking of rotation and translation of medical instruments plays a substantial role in many modern interventions.
