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Multi-view multi-scale lymph node false positive inhibition modeling method

A modeling method and false positive technology, applied in the field of medical imaging and artificial intelligence, can solve the problem of low detection accuracy, achieve the effect of slow training, reduce model overfitting, and improve robustness

Active Publication Date: 2021-04-16
SICHUAN UNIV
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Problems solved by technology

Therefore, a large number of false positive nodules are generated while detecting nodules, which causes the problem of low detection accuracy.

Method used

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  • Multi-view multi-scale lymph node false positive inhibition modeling method
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Embodiment Construction

[0033] In order to make the object, technical solution and advantages of the present invention clearer, the present invention is further described in detail. It should be understood that the specific embodiments described here are only used to explain the present invention, and are not intended to limit the present invention, that is, the described embodiments are only some of the embodiments of the present invention, but not all of the embodiments.

[0034] Such as figure 1 As shown, a multi-view and multi-scale lymph node false positive suppression modeling method includes the following steps:

[0035] Step 1. Obtain an initial CT image of the lungs through CT detection, and preprocess the initial CT image to obtain a standard CT image;

[0036]Step 2. Perform slice processing on the standard CT image to obtain a fixed-size CT slice, input the fixed-size CT slice into the candidate nodule detection model, and obtain the three-dimensional coordinates of the candidate nodule;...

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Abstract

The invention relates to a multi-view multi-scale lymph node false positive inhibition modeling method. The invention discloses a radiotherapy automatic plan design system and a construction method thereof, and relates to the field of radiotherapy plan systems, and the system comprises a plan design auxiliary contour generation module, a prescription setting module, a radiation field adding module, a deep neural network dose prediction module, and an optimization objective function generation and plan design module. The deep neural network dose prediction module is used for providing a reasonable dose design target for a reverse optimization process according to data obtained by the same disease type; after the deep neural network model is trained, the dose distribution condition of a radiotherapy patient can be quickly predicted within several minutes, and radiotherapy plan design is automatically carried out, so the working efficiency of a radiotherapy doctor is effectively improved, and the formulation of a radiotherapy scheme of the patient is accelerated.

Description

technical field [0001] The invention relates to the fields of medical imaging and artificial intelligence, in particular to a multi-view and multi-scale lymph node false positive suppression modeling method. Background technique [0002] Colorectal cancer is a common malignant tumor in the gastrointestinal tract. my country is an area with a low incidence of colorectal cancer, but this year the incidence of colorectal cancer has a significant upward trend, and the incidence and mortality are increasing day by day. At present, the most effective method is tissue biopsy under colonoscopy, but there are certain risks. Therefore, accurate and non-invasive imaging methods have become a hot topic in research. [0003] In the study of intelligent impact studies, automatic detection of lymph nodes based on deep learning is an important research direction. Popular detection algorithms in deep learning fields such as Fast-RCNN and YOLO also shine in intelligent detection of lung lymp...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06K9/62G06N3/08G06N3/04
Inventor 章毅王自强王晗伍兵张海仙黄昊王璟玲曾涵江潘震朱昱州黄月瑶张许兵刘宇航
Owner SICHUAN UNIV
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