Deep learning atrial septal defect detection method and device

A kind of atrial septal defect and deep learning technology, applied in the computer field, can solve problems such as complex image background and difficult data collection, and achieve the effect of shortening operation time, reducing false positive rate, and improving model accuracy

Pending Publication Date: 2020-11-10
HANGZHOU SHENRUI BOLIAN TECH CO LTD +2
View PDF0 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, no detection of atrial septal defect has been done so far. One of the reasons is that the image bac...

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
  • Deep learning atrial septal defect detection method and device
  • Deep learning atrial septal defect detection method and device
  • Deep learning atrial septal defect detection method and device

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0024] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0025] The core of the present invention is:

[0026] A solution applied in clinical scenarios is proposed to automatically and accurately identify atrial septal defect. The scheme adopts the method of first classifying the standard slices, then segmenting the atrium, ventricle and blood vessels, and finally detecting the atrial septal defect. Firstly, the five slices of the atrial septal defect during the moving process of the ul...

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 deep learning atrial septal defect detection method and device, and the method comprises the steps: obtaining an ultrasonic cardiogram, preprocessing the ultrasonic cardiogram, and extracting a region of interest; carrying out feature extraction on the region of interest, and identifying a subsword process atrial septal frontal section, a subsword process atrial septal sagittal section, a cardiac apex four-cavity cardiac tangent plane, a low-position paracarpal four-cavity cardiac tangent plane and a paracarpal aorta short-axis tangent plane; detecting the minimum distance point of the atrial septal defect; segmenting heart structures of a subsword atrial septal frontal section, a subsword atrial septal sagittal section, a cardiac apex four-cavity cardiac tangent plane, a low paracardial four-cavity cardiac tangent plane and a paracardial aorta minor axis section to obtain segmentation results, the heart structures including a left atrium, a right atrium, a left ventricle, a right ventricle, an aorta and pulmonary arteries; and according to the segmentation results, filtering the detected minimum distance point bounding box of the atrial septal defect, andtaking the filtered result as an atrial septal defect detection result.

Description

technical field [0001] The present invention relates to the field of computers, in particular to a deep learning method and device for detecting atrial septal defect. Background technique [0002] As a noninvasive diagnostic technique, two-dimensional echocardiography is the main imaging method for the diagnosis of atrial septal defect. The correct rate of diagnosis of congenital heart disease by echocardiography is closely related to the technical level of the operating doctor. An excellent echocardiography doctor not only has high-level operation skills, image recognition and interpretation capabilities, but also has a comprehensive understanding of various diseases of congenital heart disease. Have holistic understanding. Due to the high technical level requirements and long personnel training cycle, my country has a large population base and a shortage of pediatricians; ultrasound diagnosis of children's cardiovascular diseases is difficult, expensive, and ineffective i...

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
IPC IPC(8): G06T7/00G06T7/11G06K9/62G06N3/04G06N3/08
CPCG06T7/0012G06T7/11G06N3/08G06T2207/10132G06T2207/20024G06T2207/20081G06T2207/20084G06T2207/30048G06N3/045G06F18/241
Inventor 张玉奇赵列宾王成王蕴衡马骁杰吴兰萍洪雯静陈丽君董斌王汉松李昂俞益洲
Owner HANGZHOU SHENRUI BOLIAN TECH CO LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap