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

Medical image segmentation method based on lightweight full convolutional neural network

A convolutional neural network and medical image technology, applied in the field of image feature extraction and segmentation, can solve the problem of less research and application of full convolutional neural network model cropping, and achieve the effects of ensuring visual effects, improving computing quality, and ensuring quality

Active Publication Date: 2020-08-04
CHONGQING UNIV OF POSTS & TELECOMM
View PDF15 Cites 10 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

It is widely used in convolutional neural networks, but the research and application of model clipping for full convolutional neural networks is still relatively small

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
  • Medical image segmentation method based on lightweight full convolutional neural network
  • Medical image segmentation method based on lightweight full convolutional neural network
  • Medical image segmentation method based on lightweight full convolutional neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0044] The technical solutions in the embodiments of the present invention will be described clearly and in detail below with reference to the drawings in the embodiments of the present invention. The described embodiments are only some of the embodiments of the invention.

[0045] The technical scheme that the present invention solves the problems of the technologies described above is:

[0046] A kind of medical image segmentation method based on lightweight fully convolutional neural network, is characterized in that, comprises the following steps:

[0047] Step 1: Perform preprocessing such as grayscale, normalization, contrast-limited adaptive histogram equalization (CLAHE), and gamma correction (Gamma adjustment) on the data set to improve the segmentation quality of medical images;

[0048] Step 2: Since the amount of preprocessed data in step 1 is too small, in order to effectively avoid overfitting and falling into local optimal solutions during deep network training...

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 medical image segmentation method based on a lightweight full convolutional neural network. The method comprises the following steps: carrying out preprocessing such as graying, normalization, contrast limited adaptive histogram equalization (CLAHE) and gamma correction on a data set; randomly extracting patches from the training set and sequentially extracting patch graphs from the test set to complete data enhancement; building a full convolutional neural network architecture composed of a contraction path (left side) and an expansion path (right side), and designinga left-one-out training method for a data set with a small number of images; and finally, completing BN channel model cutting through channel sparse regularization training, cutting channels of whichscaling factors are smaller than a set threshold, finely adjusting the cut network to obtain a lightweight full convolutional neural network, and inputting test data into the network for rapid test to complete image segmentation. The lightweight full convolutional neural network not only ensures the advantage of high segmentation precision of the deep network, but also improves the test speed ofthe image segmentation network.

Description

technical field [0001] The invention belongs to the technical field of image feature extraction and segmentation methods, in particular to a medical image semantic segmentation method for a lightweight fully convolutional neural network. Background technique [0002] Fully Convolutional Neural Network (FCN) has dominated the field of image segmentation, especially semantic segmentation, because it has the characteristics of accepting input images of any size, high computing speed, etc., and can achieve pixel-level image segmentation effects. [0003] At present, a lot of experiments have been done on the fully convolutional neural network. Since FCN in 2015, a batch of classic models have been produced, such as UNet and UNet-like structures. These models are based on FCN and continuously deepen the number of network layers to improve the network. of the segmentation accuracy. But it also brings a series of problems, such as high network training environment requirements, sl...

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): G06T7/11G06T7/194G06N3/04G06N3/08
CPCG06T7/11G06T7/194G06N3/08G06T2207/20081G06T2207/20084G06T2207/20132G06T2207/30004G06N3/045
Inventor 唐贤伦钟冰李洁彭德光李锐郝博慧彭江平李伟李星辰黄淼邹密
Owner CHONGQING UNIV OF POSTS & TELECOMM
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