Motion estimation for ultrasound image sequences using deep learning

2020 ◽  
Vol 148 (4) ◽  
pp. 2487-2487
Author(s):  
Skanda Bharadwaj ◽  
Mohamed Almekkawy
Keyword(s):  
Deep Learning ◽  
Motion Estimation ◽  
Ultrasound Image ◽  
Image Sequences

Maximum likelihood motion estimation in ultrasound image sequences

1997 ◽  
Vol 4 (6) ◽  
pp. 156-157 ◽  
Author(s):  
M.G. Strintzis ◽  
I. Kokkinidis
Keyword(s):  
Maximum Likelihood ◽  
Motion Estimation ◽  
Ultrasound Image ◽  
Image Sequences

scholarly journals Deep learning-based recognition of cell structures in fluorescence microscopy sequences with respect to their morphology on cells infected with Marburg virus

2020 ◽  
Vol 6 (3) ◽  
pp. 501-504
Author(s):  
Dennis Schmidt ◽  
Andreas Rausch ◽  
Thomas Schanze
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Infected Cell ◽  
Network Architecture ◽  
Image Sequences ◽  
Marburg Virus ◽  
Microscopic Image ◽  
Cell Structures ◽  
Fast Recognition ◽  
Subviral Particles

AbstractThe Institute of Virology at the Philipps-Universität Marburg is currently researching possible drugs to combat the Marburg virus. This involves classifying cell structures based on fluoroscopic microscopic image sequences. Conventionally, membranes of cells must be marked for better analysis, which is time consuming. In this work, an approach is presented to identify cell structures in images that are marked for subviral particles. It could be shown that there is a correlation between the distribution of subviral particles in an infected cell and the position of the cell’s structures. The segmentation is performed with a "Mask-R-CNN" algorithm, presented in this work. The model (a region-based convolutional neural network) is applied to enable a robust and fast recognition of cell structures. Furthermore, the network architecture is described. The proposed method is tested on data evaluated by experts. The results show a high potential and demonstrate that the method is suitable.

scholarly journals Deep learning based keypoint rejection system for underwater visual ego-motion estimation

2020 ◽  
Vol 53 (2) ◽  
pp. 9471-9477
Author(s):  
Marco Leonardi ◽  
Luca Fiori ◽  
Annette Stahl
Keyword(s):  
Deep Learning ◽  
Motion Estimation
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