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

Medical image segmentation method, device and equipment and readable storage medium

A medical image and image segmentation technology, applied in the field of medical image processing, can solve the problems of high requirement of medical image detail, loss of medical image segmentation, affecting quantitative calculation of cardiac ejection volume, etc.

Active Publication Date: 2020-01-14
LANGCHAO ELECTRONIC INFORMATION IND CO LTD
View PDF5 Cites 12 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The segmentation of medical images requires a high degree of detail, incorrect or unstable segmentation will directly affect the quantitative calculation of cardiac ejection volume, thus losing the original meaning of medical image segmentation

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, device and equipment and readable storage medium
  • Medical image segmentation method, device and equipment and readable storage medium
  • Medical image segmentation method, device and equipment and readable storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0065] In order to facilitate those skilled in the art to better understand the medical image segmentation method provided by the embodiment of the present invention, the medical image segmentation method provided by the embodiment of the present invention will be compared and described below in conjunction with the prior art.

[0066] The input data of medical image segmentation is generally CT data or nuclear magnetic data, and a case usually contains multiple CT images or nuclear magnetic images. There are many deep learning image segmentation methods, no matter which method is used, it is necessary to design a loss function and optimize the training of the network.

[0067] In the prior art, it is assumed that the input image dimension of a case is 512×512, and there are n images in the case. For the segmentation network, the calculation of the loss function of the network model usually adopts the method of cross entropy loss, and the calculation method is as follows:

[0068] T...

Embodiment 2

[0114] In order to increase the segmentation accuracy of the central region of organs and tissues. On the basis of the first embodiment, the embodiment of the present invention can also weight the loss (loss value) of the central area to increase the proportion of the loss value of the central area.

[0115] That is, in the medical image segmentation method provided by the embodiment of the present invention, the training process of the deep learning image segmentation network may also include:

[0116] Determine the area corresponding to the label matrix in the medical image training sample as the central area of ​​the organization;

[0117] Correspondingly, when the loss function is used to calculate the loss value, the loss weight is added to the pixels corresponding to the edge enhancement region in the training segmentation result, including:

[0118] When using the loss function to calculate the loss value, increase the loss weight for the corresponding pixels in the edge enhanc...

Embodiment 3

[0130] In order to flexibly train the deep learning image segmentation network, it can also calculate the loss value corresponding to the edge enhancement area, the loss value corresponding to the center area of ​​the organization, the loss value of all areas, and weight the different area loss values ​​according to different weight ratios Calculate to obtain the overall loss value.

[0131] Please refer to Figure 4 , The overall loss value calculation process, including:

[0132] S21: Calculate the edge enhancement loss value corresponding to the edge enhancement area by using the loss weight corresponding to each pixel in the edge enhancement area;

[0133] S22: Calculate the center loss value corresponding to the center area of ​​the organization by using the loss weight of each pixel in the center area of ​​the organization;

[0134] S23. Calculate ordinary loss values ​​corresponding to all regions of the medical image training sample;

[0135] S24. Perform weighted superposition...

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 discloses a medical image segmentation method, device and equipment and a readable storage medium. The method comprises the steps: obtaining a to-be-segmented medical image, and inputting the medical image into a deep learning image segmentation network; carrying out tissue segmentation on the medical image by utilizing the target segmentation parameter which focuses on the specifiedtissue edge to obtain an image segmentation result; the training process of the deep learning image segmentation network comprises the following steps: determining an edge enhancement region of a medical image training sample by utilizing a segmentation label corresponding to the medical image training sample; inputting the medical image training sample into a deep learning image segmentation network for tissue segmentation to obtain a training segmentation result; when a loss function is used to calculate a loss value, increasing a loss weight for a pixel corresponding to an edge enhancementregion in the training segmentation result; and adjusting the segmentation parameter of the deep learning image segmentation network by using the loss value to obtain a target segmentation parameter.The method can improve the medical image segmentation precision.

Description

Technical field [0001] The present invention relates to the technical field of medical image processing, in particular to a medical image segmentation method, device, equipment and readable storage medium. Background technique [0002] Medical image segmentation is of great use in imaging diagnosis. Automatic segmentation of medical images can help doctors confirm the size of diseased tumors and quantitatively evaluate the effects before and after treatment. In addition, the boundary recognition and screening of organs and lesions is also a routine work of imaging doctors. Both CT and MRI data are three-dimensional data, which means that the segmentation of organs and lesions needs to be performed layer by layer. If it is divided manually, it will bring heavy workload to the doctor. [0003] At present, many scholars have proposed many segmentation methods for medical images. However, due to the complexity of medical images and the changeable segmentation targets, there are stil...

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): G06T7/11G06T7/13G06T5/30G06K9/62
CPCG06T7/13G06T7/11G06T5/30G06T2207/10081G06F18/214
Inventor 王立郭振华赵雅倩
Owner LANGCHAO ELECTRONIC INFORMATION IND CO LTD
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