Automatic Frame Selection using CNN in Ultrasound Elastography.Ultrasound elastography is used to estimate the mechanical properties of the tissue by monitoring its response to an internal or external force.  In this paper, we introduce a new method using a convolutional neural network (CNN) to determine the suitability of a pair of RF frames for elastography in only 5.4 ms. Our method could also be used to automatically choose the best pair of RF frames, yielding a high-quality strain image.  The CNN was trained on 3,818 pairs of RF frames, while testing was done on 986 new unseen pairs, achieving an accuracy of more than 91%. 
CNN
Automatic Frame Selection using CNN in Ultrasound Elastography.Ultrasound elastography is used to estimate the mechanical properties of the tissue by monitoring its response to an internal or external force.  In this paper, we introduce a new method using a convolutional neural network (CNN) to determine the suitability of a pair of RF frames for elastography in only 5.4 ms. Our method could also be used to automatically choose the best pair of RF frames, yielding a high-quality strain image.  The CNN was trained on 3,818 pairs of RF frames, while testing was done on 986 new unseen pairs, achieving an accuracy of more than 91%. 
CNN
Automatic Frame Selection using CNN in Ultrasound Elastography.Ultrasound elastography is used to estimate the mechanical properties of the tissue by monitoring its response to an internal or external force.  In this paper, we introduce a new method using a convolutional neural network (CNN) to determine the suitability of a pair of RF frames for elastography in only 5.4 ms. Our method could also be used to automatically choose the best pair of RF frames, yielding a high-quality strain image.  The CNN was trained on 3,818 pairs of RF frames, while testing was done on 986 new unseen pairs, achieving an accuracy of more than 91%. 
CNN
Automatic Frame Selection using CNN in Ultrasound Elastography.Ultrasound elastography is used to estimate the mechanical properties of the tissue by monitoring its response to an internal or external force.  In this paper, we introduce a new method using a convolutional neural network (CNN) to determine the suitability of a pair of RF frames for elastography in only 5.4 ms. Our method could also be used to automatically choose the best pair of RF frames, yielding a high-quality strain image.  The CNN was trained on 3,818 pairs of RF frames, while testing was done on 986 new unseen pairs, achieving an accuracy of more than 91%. 
CNN
Automatic Frame Selection using CNN in Ultrasound Elastography.Ultrasound elastography is used to estimate the mechanical properties of the tissue by monitoring its response to an internal or external force.  In this paper, we introduce a new method using a convolutional neural network (CNN) to determine the suitability of a pair of RF frames for elastography in only 5.4 ms. Our method could also be used to automatically choose the best pair of RF frames, yielding a high-quality strain image.  The CNN was trained on 3,818 pairs of RF frames, while testing was done on 986 new unseen pairs, achieving an accuracy of more than 91%. 
CNN