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A multi-view and multi-scale lymph node false positive suppression modeling method

A modeling method and false positive technology, applied in the fields of medical imaging and artificial intelligence, can solve the problem of low detection accuracy, achieve the effect of improving robustness, multi-image feature information, and reducing the amount of parameters

Active Publication Date: 2021-11-16
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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  • A multi-view and multi-scale lymph node false positive suppression modeling method
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  • A multi-view and multi-scale lymph node false positive suppression 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 and multi-scale lymph node false positive suppression modeling method. The invention discloses a radiotherapy automatic plan design system and a construction method thereof, relating to the field of radiotherapy plan systems, including a plan design auxiliary contour generation module, a prescription setting module, a field addition module, a deep neural network dose prediction module and an optimization target Function generation and planning design module; the deep neural network dose prediction module is used to provide a reasonable dose design target for the reverse optimization process according to the data obtained by the same disease; after the deep neural network model of the present invention is trained, it can Rapidly predict the dose distribution of radiotherapy patients within a few minutes, and automatically design the radiotherapy plan, which effectively improves the work efficiency of radiologists and accelerates the formulation of radiotherapy plans for patients.

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 Patents(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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