Intensity modulated radiation therapy plan three-dimensional dose distribution prediction method based on deep network learning

An intensity-modulated radiotherapy and three-dimensional dose technology, applied in neural learning methods, radiotherapy, biological neural network models, etc., can solve problems such as incomplete description of anatomical information, inability to predict multiple regions of interest at the same time, and avoid manual extraction The effect of incomplete information and improved accuracy

Active Publication Date: 2019-08-02
SOUTHERN MEDICAL UNIVERSITY
View PDF7 Cites 8 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to overcome the defects of the above-mentioned prior art, and provide a three-dimensional dose distribution prediction method suitable for intensity-modulated

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
  • Intensity modulated radiation therapy plan three-dimensional dose distribution prediction method based on deep network learning
  • Intensity modulated radiation therapy plan three-dimensional dose distribution prediction method based on deep network learning
  • Intensity modulated radiation therapy plan three-dimensional dose distribution prediction method based on deep network learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0052] In order to make the purpose, technical solution, design method and advantages of the present invention clearer, the present invention will be further described in detail through specific embodiments in conjunction with the accompanying drawings. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0053] In all examples shown and discussed herein, any specific values ​​should be construed as exemplary only, and not as limitations. Therefore, other instances of the exemplary embodiment may have different values.

[0054] Techniques, methods and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, such techniques, methods and devices should be considered part of the description.

[0055] According to an embodiment of the present invention, a method for predicting the three-dimensional dose distribution of an...

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 an intensity modulated radiation therapy plan three-dimensional dose distribution prediction method based on deep learning. The intensity modulated radiation therapy plan three-dimensional dose distribution prediction method comprises the following steps: collecting effective intensity modulated radiation therapy plan data to form a case database; extracting three-dimensional anatomical structure contour features of the region of interest of each patient from the case database; dividing the three-dimensional anatomical structure contour of the region of interest of the patient into a plurality of two-dimensional contour slice graphs; extracting dose features of each patient from the case database, and dividing the dose features into a plurality of two-dimensional dose slice distribution graphs; establishing a deep convolutional network, inputting the two-dimensional contour slice graph and the corresponding two-dimensional dose slice distribution graph of the patient, and obtaining an association model between anatomical structure contour features and dose features through model training; and predicting the three-dimensional dose distribution of the new patient by using the trained association model. By means of the intensity modulated radiation therapy plan three-dimensional dose distribution prediction method, the incidence relation between the anatomical structure features and the dose features can be effectively obtained, and the accuracy of dose prediction is improved.

Description

technical field [0001] The invention relates to the technical field of intelligent radiotherapy, in particular to a method for predicting three-dimensional dose distribution in an intensity-modulated radiotherapy plan based on deep network learning. Background technique [0002] Tumor radiotherapy, with its unique advantages, has become one of the main means of tumor treatment proposed by the World Health Organization. Its main goal is to reduce the dose deposition of surrounding normal tissues as much as possible while ensuring that the target area can reach a specific dose. Improve the local control rate of tumor. Intensity-modulated radiation therapy, namely intensity-modulated conformal radiation therapy, is a kind of three-dimensional conformal radiation therapy, which requires the dose intensity in the radiation field to be adjusted according to certain requirements. It makes the distribution of radiotherapy dose consistent with the shape of the target area, and the t...

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): G16H20/40G16H50/70G16H50/50G06N3/04G06N3/08A61N5/10
CPCG16H20/40G16H50/70G16H50/50G06N3/08A61N5/1031A61N2005/1041G06N3/045
Inventor 宋婷郭芙彤周凌宏吴艾茜贾启源亓孟科
Owner SOUTHERN MEDICAL UNIVERSITY
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