公开(公告)号：US20240320833A1

公开(公告)日：2024-09-26

申请号：US18199377

申请日：2023-05-19

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY CHINESE ACADEMY OF SCIENCES

Inventor： Yingying HAN ,  Ying HU ,  Baoliang ZHAO ,  Yuxin SONG ,  Ziwen WANG ,  Peng ZHANG

IPC: G06T7/00 ,  G06T7/11

CPC classification number: G06T7/0016 ,  G06T7/11 ,  G06T2207/10132

Abstract: An ultrasonic image quality quantitative evaluation method includes segmenting a focus area aiming at a target ultrasonic image to extract a region of interest and perform a masking operation to acquire an image segmentation result mask; taking the image segmentation result mask as a reference image, and quantitatively comparing the reference image with a corresponding focus area according to a set evaluation standard to acquire a plurality of evaluation result indexes; and inputting the plurality of evaluation result indexes as an image feature into a classifier to acquire a quality quantification result of the focus area of the target ultrasonic image, where the classifier takes the plurality of evaluation result indexes corresponding to a sample image as an input feature, takes an image quality label of the labeled focus area as an output, and acquires the quality quantification result through training based on a set loss function.

公开(公告)号：US20230386048A1

公开(公告)日：2023-11-30

申请号：US18366706

申请日：2023-08-08

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY CHINESE ACADEMY OF SCIENCES

Inventor： Yuxin SONG ,  Baoliang ZHAO ,  Ying HU ,  LONG LEI

IPC: G06T7/149

CPC classification number: G06T7/149 ,  G06T2207/10132 ,  G06T2207/20081 ,  G06T2207/10072

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.