A full-modal medical image sequence grouping method based on a deep learning sign structure. The method comprises: acquiring medical image information; performing information extraction on the acquired medical image information; establishing a full-modal deep learning AI sequence matching system; performing sequence matching processing; transmitting processed medical image sequences to a display unit in groups; and the display unit displaying full-modal medical image sequences in groups. A deep learning neural network is used, human skeletons are precisely identified and segmented into relatively fixed local regions according to precise CT and MR anatomy information of a human body, precise human body position segmentation is performed using specified skeleton parts, precise positioning is performed according to a CT or MR image in dual modalities of a molecular image, the CT or MR image is converted to a corresponding layer of a full-modal image, and automatic and precise registration and display is performed, such that diagnosis errors caused by a technical level difference are reduced, and the working efficiency of physicians is also improved.
FULL-MODAL MEDICAL IMAGE SEQUENCE GROUPING METHOD BASED ON DEEP LEARNING SIGN STRUCTURE