Medical image processing method, apparatus, and system

A medical image processing apparatus, characterized by comprising:

• an acquisition unit, which acquires a first scout image obtained after a scout scan is performed on a subject to be examined;
• a first determination unit, which determines, according to a preset correspondence between a scout image and a section image, a predicted section image corresponding to a first region of interest in the first scout image;
• and
• a display unit, which displays the predicted section image and scanning parameters corresponding to the predicted section image
• a third determination unit, which determines the correspondence on the basis of a deep learning algorithm or a machine learning algorithm
• an acquisition module, which acquires training data, the training data comprising training input data and training output data, the training input data comprising a scout image obtained by performing a scout scan in advance on a subject under examination, and the training output data comprising a section image obtained by performing an axial scan or a spiral scan on a region of interest of the subject under examination or an index identifier of the section image;
• and
• a training module, which uses the training data to train a neural network model so as to obtain the correspondence.

A medical image processing system, characterized by comprising:

• a scan device, configured to perform a scout scan on a subject to be examined so as to obtain a first scout image;
• a processor, which determines, according to a preset correspondence between a scout image and a section image, a predicted section image corresponding to a first region of interest in the first scout image, wherein the processor is configured to determine, on the basis of a deep learning algorithm or a machine learning algorithm, a correspondence between a scout image and a section image by acquiring training data comprising training input data and training output data, the training input data comprising a scout image obtained by performing a scout scan in advance on a subject under examination, and the training output data comprising a section image obtained by performing an axial scan or a spiral scan on a region of interest of the subject under examination or an index identifier of the section image, and using the training data to train a neural network model so as to obtain the correspondence, and further configured to determine, according to the correspondence, a predicted section image corresponding to a first region of interest in the first scout image;
• and
• a display, which displays the predicted section image and scanning parameters corresponding to the predicted section image.