公开(公告)号：US20210386398A1

公开(公告)日：2021-12-16

申请号：US17274789

申请日：2019-10-24

Applicant: HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY

Inventor： Ming YUCHI ,  Mingyue DING ,  Qiude ZHANG ,  Junjie SONG ,  Kuolin LIU ,  Zhaohui LIU ,  Liang ZHOU ,  Zhaohui QUAN ,  Xiaoyue FANG ,  Yun WU ,  Quan ZHOU

IPC: A61B8/14 ,  A61B8/00 ,  A61B8/08

Abstract: A spiral ultrasonic tomography imaging method and system are provided. The method is to use synchronous rotation of an ultrasonic probe or cyclic switching of emission array elements in the ultrasonic probe under the premise of a uniform displacement of the ultrasonic probe along the Z axis, so that the change trajectory of the positions of emission array elements over time in a three-dimensional space at each ultrasonic emission time is distributed along a spiral line or a curve. During the process of the uniform displacement of the ultrasonic probe along the Z axis, the ultrasonic probe emits ultrasonic waves and receives and collects echo data. The collected echo data is stored and post-processed to realize ultrasonic tomography imaging of an object. The spiral ultrasonic tomography three-dimensional scanning method and the corresponding system realize rapid continuous uninterrupted data acquisition to ensure that higher spatial resolution in the Z-axis direction.

公开(公告)号：US20240257356A1

公开(公告)日：2024-08-01

申请号：US18635023

申请日：2024-04-15

Applicant: HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY

Inventor： Xuming ZHANG ,  Mingwei WEN ,  Quan ZHOU

IPC: G06T7/11 ,  G06T3/4046 ,  G06T7/00 ,  G06T7/143 ,  G16H30/40

CPC classification number: G06T7/11 ,  G06T3/4046 ,  G06T7/0012 ,  G06T7/143 ,  G16H30/40 ,  G06T2200/04 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20132 ,  G06T2207/30096

Abstract: The disclosure belongs to the field of image segmentation in medical image processing and discloses a three-dimensional medical image segmentation method and system based on short-term and long-term memory self-attention models, in which the method can segment a target area image in the medical image, which includes the following. (1) A training set sample is established. (2) Processing is performed on the original three-dimensional medical image to be segmented to obtain a sample to be segmented. (3) A three-dimensional medical image segmentation network based on short-term and long-term memory self-attention is established and trained. (4) The sample to be segmented is input to the network, and then a segmentation result of the target area in the sample to be segmented is output. By combining CNN and Transformer, a new model for accurate real-time segmentation of the target area (such as a tumor) in the three-dimensional medical image is obtained.