Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

System and device for improving segmentation accuracy of left ventricles of multiple heart views

A high-precision, left-ventricular technology, applied in the field of medical testing, can solve problems such as inaccurate segmentation of the left ventricle, and achieve the effects of improving segmentation accuracy, reducing workflow, and improving training accuracy

Active Publication Date: 2020-10-02
SHANDONG UNIV
View PDF6 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At the same time, the echocardiogram also contains a lot of noise, and the existing segmentation algorithm cannot accurately segment the left ventricle.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • System and device for improving segmentation accuracy of left ventricles of multiple heart views
  • System and device for improving segmentation accuracy of left ventricles of multiple heart views
  • System and device for improving segmentation accuracy of left ventricles of multiple heart views

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0025] In order to solve the above problems, as attached Figure 1-3 As shown, the present invention proposes a left ventricular myocardium segmentation system and device based on a deep learning segmentation method, and the system can automatically segment the left ventricular myocardium under different views.

[0026] A deep learning-based system to improve the accuracy of left ventricle segmentation in multiple cardiac views, including:

[0027] The data collection module is configured to: collect image data of several echocardiograms with different views to form an original image data set; collect the echocardiograms to be processed as data to be segmented;

[0028] The preprocessing module is configured to: perform preprocessing on the original image data set to form an experimental data set;

[0029] The training module is configured to: build a deep neural network training model, input the experimental data set into the training model for training, and when the loss fu...

Embodiment 2

[0044] A heart multi-view left ventricular myocardium segmentation system based on deep learning, including:

[0045] Acquisition module: Take the patient as the unit, collect medical images of the apical second chamber and apical fourth chamber of the echocardiogram, mark the outline of the left ventricular myocardium in different views, and make it into an original image data set;

[0046] Preprocessing module: preprocess the data set to obtain the experimental data set;

[0047] Training module: input the experimental data set into the deep learning RetinaNet network to obtain the heart view recognition result and left ventricle detection result, and input the detection result to the segmentation network to obtain the segmentation result.

[0048] Further, the method for making the original image data set is specifically:

[0049] Taking the patient as the unit, first derive the images of the apical second chamber, apical third chamber, and apical fourth chamber from the e...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention provides a system and device for improving the segmentation accuracy of left ventricles of multiple heart views based on deep learning, and the system comprises a data collection modulewhich is configured to collect the image data of ultrasonic cardiograms of a plurality of different views to form an original image data set, and collecting a to-be-processed echocardiogram as to-be-segmented data; a preprocessing module which is configured to preprocess the original image data set to form an experimental data set; a training module which is configured to construct a deep neural network training model, input the experimental data set into the training model for training, and when a loss function value in the training model is not reduced any more, stop the training of the training model and save model parameters; a data processing module which is configured to input a to-be-processed echocardiogram into the training module for storing the model parameters to obtain an endocardial and epicardial segmentation result. According to the invention, the training precision of processing different ventricular images is improved, and then the heart view segmentation precision isimproved.

Description

technical field [0001] The invention belongs to the technical field of medical detection, and in particular relates to a system and device for improving the segmentation accuracy of left ventricle in multiple heart views based on deep learning. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] With the development of medical technology, a variety of medical imaging data have emerged. How to correctly, quickly and maximize the use of these medical imaging data to diagnose diseases has become a hot spot in today's society. [0004] Machine learning techniques enable researchers to develop and utilize complex models to classify or predict various abnormalities or diseases or to identify and segment medical lesions. Nowadays, the development of machine learning technology is gradually mature and perfect. Deep learning is a new field of machine...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/10
CPCG06T7/0012G06T7/10G06T2207/30048G06T2207/20081G06T2207/20084G06T2207/10132
Inventor 刘治崔笑笑肖晓燕
Owner SHANDONG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com