Abstract: The present disclosure relates to the field of image processing technologies, and provides an ultrasound image segmentation method and apparatus, a terminal device, and a storage medium. With the method, simulated ultrasound images are synthesized based on Computed Tomography (CT) images. An image segmentation model is pre-trained using the synthesized simulated ultrasound images. The pre-trained image segmentation model is migrated, by employing a transfer learning method, to real sample ultrasound images for further training to obtain a final image segmentation model. A segmentation processing on an ultrasound image to be segmented is completed by the final image segmentation model. In this way, the ultrasound images synthesized based on the CT images can be used to replace a part of training data, thereby solving a problem of lack of training data when training the image segmentation model.

Abstract: The present disclosure relates to the technical field of image processing and provides a motion estimation method and apparatus for a tumor, a terminal device, and a storage medium. The method includes: obtaining a current value of a respiratory correlation signal of a patient, the respiratory correlation signal reflecting a motion trajectory feature of a designated organ with the tumor of the patient in each of different respiratory states; inputting the current value of the respiratory correlation signal into a tumor motion estimation model to obtain an estimated current position of the tumor; and inputting the estimated current position of the tumor into an organ motion estimation model to obtain an estimated current velocity vector field of the designated organ. By adopting the motion estimation method, it realizes a respiratory motion estimation for the whole organ and enhance accuracy of positioning the tumor and vital anatomical structures around the tumor.

Abstract: The present disclosure relates to the field of image processing technologies, and provides an ultrasound image segmentation method and apparatus, a terminal device, and a storage medium. With the method, simulated ultrasound images are synthesized based on Computed Tomography (CT) images. An image segmentation model is pre-trained using the synthesized simulated ultrasound images. The pre-trained image segmentation model is migrated, by employing a transfer learning method, to real sample ultrasound images for further training to obtain a final image segmentation model. A segmentation processing on an ultrasound image to be segmented is completed by the final image segmentation model. In this way, the ultrasound images synthesized based on the CT images can be used to replace a part of training data, thereby solving a problem of lack of training data when training the image segmentation model.

Abstract: The present disclosure relates to the technical field of image processing and provides a motion estimation method and apparatus for a tumor, a terminal device, and a storage medium. The method includes: obtaining a current value of a respiratory correlation signal of a patient, the respiratory correlation signal reflecting a motion trajectory feature of a designated organ with the tumor of the patient in each of different respiratory states; inputting the current value of the respiratory correlation signal into a tumor motion estimation model to obtain an estimated current position of the tumor; and inputting the estimated current position of the tumor into an organ motion estimation model to obtain an estimated current velocity vector field of the designated organ. By adopting the motion estimation method, it realizes a respiratory motion estimation for the whole organ and enhance accuracy of positioning the tumor and vital anatomical structures around the tumor.