Vertebra positioning method based on convolutional neural network

A technology of convolutional neural network and positioning method, which is applied in the direction of neural learning method, biological neural network model, neural architecture, etc., can solve the problems of increasing the difficulty of locating the spine, complex organizational structure, etc., to reduce the detection area and improve the accuracy Effect

Inactive Publication Date: 2018-05-01
HARBIN UNIV OF SCI & TECH
View PDF2 Cites 34 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The adjacent tissue structure of the spine is complex, and the background texture is rich, which also makes it more difficult to locate the spine

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
  • Vertebra positioning method based on convolutional neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0026] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail with reference to the accompanying drawings.

[0027] Step S1: first preprocess the spine CT image to be detected, remove dryness, and enhance the contrast of the region to be detected.

[0028] Step S2: Train the Faster R-CNN model, which is divided into two steps of training, the training of the RPN network and the training of the Fast R-CNN network. The RPN network obtains the candidate area of ​​the target area, and the Fast R-CNN network is used for target detection. Input the preprocessed spine CT image and the candidate area image of the target area obtained through the RPN network into the Fast R-CNN detector for secondary training of the model, and finally obtain the vertebral positioning model.

[0029] Step S3: Use the kernel density estimation method to find the centroid of the entire region from the fram...

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 relates to a vertebra positioning method based on a convolutional neural network. Vertebra positioning is an effective way for judging spinal diseases, because tissues around the vertebra have complex structures and abundant textures, diagnosis is difficult, and the efficiency is low. A vertebra positioning method based on a convolutional neural network is provided based on the aboveproblem, and therefore vertebra positioning is more accurate. The vertebra positioning method comprises following steps: A, processing vertebra CT data to increase the contrast ratio; B, carrying outsegmentation and extraction on a key area in a vertebra CT picture by means of a Faster R-CNN model; C, predicting the location of the center of mass of each segmented vertebra by means of a kernel density estimation method; and D, carrying out three-dimensional reconstruction on the processed vertebra CT picture by means of the Mimics.

Description

technical field [0001] The invention mainly relates to the field of medical image processing, in particular to a method for vertebral positioning based on a convolutional neural network. Background technique [0002] Medical imaging technology has experienced more than 100 years of development. Ultrasound, CT, MRI, PET, etc. have been widely used in clinical practice. Along with the development, popularization and application of computer technology, medical image processing technology has also developed rapidly. It not only improves the image quality and diagnosis efficiency, but also uses image information for interventional treatment and surgical guidance, and can also be used as an evaluation standard and means to quantitatively evaluate and evaluate the effect of disease treatment and surgery. [0003] Studies have shown that 80% of people in our country are troubled by spinal diseases. The automatic positioning of vertebrae in spinal imaging is a key component of image ...

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): G06T7/11G06T7/00G06T17/00G06N3/08G06N3/04A61B6/03A61B6/00
CPCG06N3/08G06T7/0012G06T7/11G06T17/00A61B6/032A61B6/505G06T2207/20081G06T2207/10081G06T2207/10088G06T2207/30012G06N3/045
Inventor 刘侠孙艳楠王倩倩
Owner HARBIN UNIV OF SCI & TECH
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