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Titlebook: Simulation, Image Processing, and Ultrasound Systems for Assisted Diagnosis and Navigation; International Worksh Danail Stoyanov,Zeike Tayl

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發(fā)表于 2025-3-25 04:54:43 | 只看該作者
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發(fā)表于 2025-3-25 11:12:42 | 只看該作者
0302-9743 l Workshop on Bio-Imaging and Visualization for Patient-Customized Simulations, BIVPCS 2017, the International Workshop on Correction of Brainshift with Intra-Operative Ultrasound, CuRIOUS 2018, and the International Workshop on Computational Precision Medicine, CPM 2018, held in conjunction with th
23#
發(fā)表于 2025-3-25 13:41:53 | 只看該作者
A Training Tool for Ultrasound-Guided Central Line Insertion with Webcam-Based Position Trackingxamined. We found that across the ultrasound image the calibration error was less than 2?mm. In a small feasibility study, two participants performed 5 needle insertions each with an average of slightly above 2?mm error. CONCLUSION: We conclude that the accuracy of the system is sufficient for clinician training.
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發(fā)表于 2025-3-25 16:53:15 | 只看該作者
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發(fā)表于 2025-3-25 20:19:03 | 只看該作者
Holographic Visualisation and Interaction of Fused CT, PET and MRI Volumetric Medical Imaging Data Ulity (MR) headset. The hologram can be manipulated with hand gestures and voice commands through the following interaction features: instantaneous visualisation and manipulation of 3D scans with a frame rate of 30?fps and a delay lower than 120 ms. These performances give a seamless interactive experience for the user?[.].
26#
發(fā)表于 2025-3-26 01:29:53 | 只看該作者
Recent Advances in Point-of-Care Ultrasound Using the , for Real-Time Image Analysisrietary . software and corresponding software development kit (SDK). These include 3D reconstruction of arbitrary untracked 2D US clips, image filtering and classification, speed-of-sound calibration and live acquisition parameter tuning in a visual servoing fashion.
27#
發(fā)表于 2025-3-26 05:41:37 | 只看該作者
28#
發(fā)表于 2025-3-26 11:41:29 | 只看該作者
GLUENet: Ultrasound Elastography Using Convolutional Neural Network finer displacement exploiting all the information of all the samples of RF data simultaneously. The coarse displacement estimate generated by CNN is substantially more robust than the Dynamic Programming (DP) technique used in GLUE for finding the coarse displacement estimates. Our results validate
29#
發(fā)表于 2025-3-26 13:35:45 | 只看該作者
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發(fā)表于 2025-3-26 18:45:28 | 只看該作者
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