Abstract: The present disclosure discloses a panoramic stitching method, an apparatus, and a storage medium. A transformation matrix obtaining method includes: obtaining motion data detected by sensors, wherein the sensors are disposed on a probe used to collect images, and the motion data is used to represent a moving trend of the probe during image collection; inputting the motion data into a pre-trained neural network, to calculate matrix parameters by using the neural network; calculating a transformation matrix by using the matrix parameters, wherein the transformation matrix is used to stitch images collected by the probe, to obtain a panoramic image. In the present disclosure, the transformation matrix can be calculated and the images can be stitched without using characteristics of the images, and factors such as brightness and the characteristics of the images do not impose an impact, thereby improving transformation matrix calculation accuracy, and improving an image stitching effect.

Abstract: A method for optimizing an ultrasonic imaging system parameter based on deep learning, comprising the following steps: step 1: collecting samples for training neural networks, the samples comprising ultrasound image samples i, and a corresponding ultrasonic imaging system parameter vector sample p used by an ultrasonic imaging system when the ultrasonic image samples are collected; step 2: establishing a neural network model and training the neural networks to convergence by using the samples collected=in step 1, so as to obtain a trained neural network system onn; and step 3: taking the original ultrasonic imaging system parameter vector p or the original ultrasonic image as an input to be input into the neural network system onn trained in step 2, at this moment, a parameter obtained from an output end of onn being an optimized ultrasonic imaging system parameter vector ep=onn(p).

Abstract: The present disclosure discloses a panoramic stitching method, an apparatus, and a storage medium. A transformation matrix obtaining method includes: obtaining motion data detected by sensors, wherein the sensors are disposed on a probe used to collect images, and the motion data is used to represent a moving trend of the probe during image collection; inputting the motion data into a pre-trained neural network, to calculate matrix parameters by using the neural network; calculating a transformation matrix by using the matrix parameters, wherein the transformation matrix is used to stitch images collected by the probe, to obtain a panoramic image. In the present disclosure, the transformation matrix can be calculated and the images can be stitched without using characteristics of the images, and factors such as brightness and the characteristics of the images do not impose an impact, thereby improving transformation matrix calculation accuracy, and improving an image stitching effect.

Abstract: The present invention relates to the technical field of image processing, in particular to a method for determining a cardiac cycle and ultrasonic equipment. The method comprises: acquiring a cardiac ultrasound video; classifying the cardiac ultrasound video by using a section type recognition model to determine a section type of the cardiac ultrasound video; and processing the cardiac ultrasound video by using a systole and diastole recognition model corresponding to the section type to obtain the cardiac cycle corresponding to the cardiac ultrasound video. The model is used to process the cardiac ultrasound video to detect the corresponding cardiac cycle. Model detection can avoid the use of an electrocardiograph and simplify the detection of the cardiac cycle. Furthermore, real-time detection of the cardiac cycle can be realized during echocardiography.

Abstract: The present invention provides an ultrasonic guidance auxiliary device for a needle, including: a probe, used for emitting and receiving an ultrasonic signal for a tissue to be punctured-for-injection; a first mechanical arm, used for moving the probe to the tissue to be punctured-for-injection; a second mechanical arm, used for fixing a puncture needle, moving the needle to be close to the tissue to be punctured-for-injection, and then causing the needle to puncture the tissue to be punctured-for-injection; and a main unit, used for moving, according to controlling of the first mechanical arm, the probe to the tissue to be punctured-for-injection, wherein the probe emits and receives the ultrasonic signal. The main unit synthesizes an ultrasonic image through the ultrasonic signal transmitted by the probe, and the main unit at least calculates a puncture distance and a puncture angle between the needle and the tissue to be punctured-for-injection through the ultrasonic image.

Abstract: A method for optimizing an ultrasonic imaging system parameter based on deep learning, comprising the following steps: step 1: collecting samples for training neural networks, the samples comprising ultrasound image samples i, and a corresponding ultrasonic imaging system parameter vector sample p used by an ultrasonic imaging system when the ultrasonic image samples are collected; step 2: establishing a neural network model and training the neural networks to convergence by using the samples collected=in step 1, so as to obtain a trained neural network system onn; and step 3: taking the original ultrasonic imaging system parameter vector p or the original ultrasonic image as an input to be input into the neural network system onn trained in step 2, at this moment, a parameter obtained from an output end of onn being an optimized ultrasonic imaging system parameter vector ep=onn(p).

Abstract: An ultrasound imaging apparatus configured with a touch screen display and computer aided measurement and/or diagnosis capability. In response to a touch input, image parameters for the computer aided measurement and/or diagnosis are determined from an analysis of the ultrasound image. A touch screen user interface allows the user to further adjust the parameters and/or other system parameters for the computer aided measurement and/or diagnosis in an interactive way. The computer aided measurement can be used directly for the computer aided diagnosis for improved accuracy and user experience.

Abstract: An ultrasound imaging apparatus configured with a touch screen display and computer aided measurement and/or diagnosis capability. In response to a touch input, image parameters for the computer aided measurement and/or diagnosis are determined from an analysis of the ultrasound image. A touch screen user interface allows the user to further adjust the parameters and/or other system parameters for the computer aided measurement and/or diagnosis in an interactive way. The computer aided measurement can be used directly for the computer aided diagnosis for improved accuracy and user experience.

Abstract: An ultrasound imaging apparatus configured with a touch screen display that includes an image area displaying at least a portion of an ultrasound image. The image area of the touch screen is configured to receive touch input. In response to the touch input, the ultrasound image can be adjusted, manipulated, edited, processed and/or measured. For example, certain properties of the ultrasound image can be quantified or determined, and used to intelligently identify a context-sensitive function to be invoked as a response to the touch input in the image area Thus, the control buttons in the control area can be simplified; and the user interface is more user friendly than a conventional user interface for ultrasound imaging.