Automatic organ-at-risk sketching method and device based on neural network and storage medium
What is Al technical title?
Al technical title is built by PatSnap Al team. It summarizes the technical point description of the patent document.
A neural network, organ technology
Active Publication Date: 2019-10-08
BEIJING LINKING MEDICAL TECH CO LTD
View PDF9 Cites 20 Cited by
Summary
Abstract
Description
Claims
Application Information
AI Technical Summary
This helps you quickly interpret patents by identifying the three key elements:
Problems solved by technology
Method used
Benefits of technology
Problems solved by technology
Due to the characteristics of fast imaging speed, high spatial accuracy and high resolution, CT images are usually used to formulate radiotherapy plans. Doctors need to accurately delineate each organ at risk in each CT slice, which is a time-consuming and laborious process. In addition, , due to the low contrast of soft tissues in CT images, for example, the parotid gland has no clear boundary and complex shape, which makes it error-prone and time-consuming for doctors to draw manually, so an accurate and fast automatic organ-at-risk segmentation algorithm is needed to assist Physicians delineate organs at risk, reducing physical labor and time in the planning pr
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
Click on the blue label to locate the original text in one second.
Reading with bidirectional positioning of images and text.
Smart Image
Examples
Experimental program
Comparison scheme
Effect test
Embodiment 2
[0122] The invention also provides a computing device, comprising:
[0123] one or more processors;
[0124] storage; and
[0125] One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs include the above-mentioned three-cascade volume-based Instructions for the method for automatically delineating organs at risk by the product neural network, the method includes the following steps:
[0126] (1) Input 3D medical images;
[0127] (2) Preprocessing the 3D medical image;
[0128] (3) Input the preprocessed three-dimensional medical image into the first-level network of the trained three-cascade convolutional neural network to identify the cross-section of the organ to be segmented;
[0129] (4) Input the cross-section screened by the first-level network into the second-level network of the trained three-cascade convolutional neural network, and roughly locate the reg...
Embodiment 3
[0135] The present invention also provides a computer-readable storage medium that stores one or more programs, and the one or more programs include instructions, and the instructions are suitable for being loaded by the memory and executing the above-mentioned three-cascade convolutional neural network-based A method for automatically delineating organs at risk, the method comprising the following steps:
[0136] (1) Input 3D medical images;
[0137] (2) Preprocessing the 3D medical image;
[0138] (3) Input the preprocessed three-dimensional medical image into the first-level network of the trained three-cascade convolutional neural network to identify the cross-section of the organ to be segmented;
[0139] (4) Input the cross-section screened by the first-level network into the second-level network of the trained three-cascade convolutional neural network, and roughly locate the region of interest of the organ to be segmented;
[0140] (5) Standardize the region of inter...
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
Login to view more
Abstract
The invention belongs to the technical field of medical images, and relates to an automatic organ-at-risk sketching method and device based on a three-level convolutional neural network, and a storagemedium. The method comprises the following steps of: preprocessing the three-dimensional medical image, inputting the preprocessed three-dimensional medical image into the first-stage network, the second-stage network and the third-stage network of the trained three-stage convolutional neural network, sequentially identifying the cross section of the organ to be segmented, coarsely positioning the region of interest of the organ to be segmented, and classifying all pixel points in the region of interest; and then outputting a three-dimensional binary segmentation result; carrying out post-processing, edge extraction and edge smoothing on the binary segmentation result to obtain an automatically sketched organ. The three-level cascade convolutional neural network model is formed by cascading three convolutional neural networks, namely a first-level network, a second-level network and a third-level network. The three-level joint neural network has the advantages that priori knowledge isnot needed, the algorithm generalization ability is good, the robustness is high, the speed is high, full automation is achieved, and the segmentation accuracy is high.
Description
technical field [0001] The invention belongs to the field of medical imaging and computer technology, and relates to a method, device and storage medium for automatically delineating organs at risk based on a three-cascade convolutional neural network. Background technique [0002] Radiation therapy is one of the three major methods of cancer treatment. It can destroy the DNA chain of cancer cells through ionizing radiation, and then lead to the death of cancer cells. In order to reduce the impact of radiation on normal tissues during treatment, doctors need to make a careful radiotherapy plan before radiotherapy. Due to the characteristics of fast imaging speed, high spatial accuracy and high resolution, CT images are usually used to formulate radiotherapy plans. Doctors need to accurately delineate each organ at risk in each CT slice, which is a time-consuming and laborious process. In addition, , due to the low contrast of soft tissues in CT images, for example, the paro...
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
Application Date:The date an application was filed.
Publication Date:The date a patent or application was officially published.
First Publication Date:The earliest publication date of a patent with the same application number.
Issue Date:Publication date of the patent grant document.
PCT Entry Date:The Entry date of PCT National Phase.
Estimated Expiry Date:The statutory expiry date of a patent right according to the Patent Law, and it is the longest term of protection that the patent right can achieve without the termination of the patent right due to other reasons(Term extension factor has been taken into account ).
Invalid Date:Actual expiry date is based on effective date or publication date of legal transaction data of invalid patent.