Method and system for grading and managing detection of pulmonary nodes based on in-depth learning

A deep learning, pulmonary nodule technology, applied in informatics, medical informatics, electrical digital data processing, etc., can solve the problem of few references, the diagnosis effect cannot reach the diagnostic efficiency of clinicians, and the clinical treatment of nodules lacks guiding value, etc. problems, to achieve the effect of scientific hierarchical management and diagnosis

Active Publication Date: 2017-08-29
SICHUAN CANCER HOSPITAL +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0009] The deep neural convolutional network used for the diagnosis and classification of lung cancer disclosed in the above invention uses unsupervised training to diagnose pulmonary nodules, which has a certain role in auxiliary diagnosis in clinical use, but it does not affect the experience of clinicians

Method used

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  • Method and system for grading and managing detection of pulmonary nodes based on in-depth learning
  • Method and system for grading and managing detection of pulmonary nodes based on in-depth learning
  • Method and system for grading and managing detection of pulmonary nodes based on in-depth learning

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Embodiment 1

[0050] like figure 1 , the present invention is a method for detecting, grading and managing pulmonary nodules based on deep learning, comprising the following steps:

[0051] S100: Collect ultra-low-dose spiral CT thin-slice images of the chest of several patients to form a CT image set, delineate the lung area in each CT image, and mark all the lung nodules in the lung area, and divide the lung nodules for grades 1-4;

[0052] S200: Based on the collected CT image set, as well as the lung area delineated in the CT image set, the marked pulmonary nodule and the grading information, sequentially train the lung area segmentation network, the suspected pulmonary nodule detection network, and the lung nodule screening and grading network; detect All CT images containing pulmonary nodules are obtained to obtain a CT image set of pulmonary nodules;

[0053] S300: Follow up the patients corresponding to the CT image set of pulmonary nodules, obtain the CT image sequences of the pa...

Embodiment 2

[0068] A pulmonary nodule detection, grading and management system based on deep learning, including a pulmonary nodule detection and grading module and a pulmonary nodule management module;

[0069] The pulmonary nodule detection and classification module includes a lung region segmentation network, a suspected pulmonary nodule detection network and a pulmonary nodule screening and classification network, which are used to accurately detect all pulmonary nodules from the image;

[0070] The lung region segmentation network is used to segment lung regions from chest low-dose spiral CT images;

[0071] The suspected pulmonary nodule detection network is used to detect suspected pulmonary nodules in the lung region;

[0072] The pulmonary nodule screening and grading network is used for screening and grading suspected pulmonary nodules;

[0073] The pulmonary nodule management module includes a pulmonary nodule management database and a lung cancer diagnosis network for diagnos...

Embodiment 3

[0077] like Figure 4 , for the detected pulmonary nodules, they are graded according to the clinical risk of the nodules, and the pulmonary nodules are divided into grades 1-4; if S / PS<5mm, and NS<8mm, they are classified as grade 1; if S / PS 》5mm, and NS》8mm, it is classified as grade 2; if S / PS》15mm or NS》15mm, it is classified as grade 3; where S: solid nodule; PS: partially solid nodule; NS: non-solid nodule nodules;

[0078] The grade 2 nodules will be reexamined after 3 months. If there is no change, they will be classified as grade 1. If the nodules increase, they will be consulted by multidisciplinary senior physicians to decide whether to enter clinical intervention. If no intervention is required, they will be classified as grade 1. Grade 1, if intervention is required, grade 4;

[0079] The grade 3 nodule will be re-examined one month after clinical treatment. If it is completely absorbed, it will be classified as grade 1. If it is not absorbed, it will be consult...

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Abstract

The invention discloses a method for grading and managing detection of pulmonary nodes based on in-depth learning. The method for grading and managing detection of the pulmonary nodes based on in-depth learning is characterized by comprising the steps of S100, collecting a chest ultralow-dose-spiral CT thin slice image, sketching a lung area in the CT image, and labeling all pulmonary nodes in the lung area; S200, training a lung area segmentation network, a suspected pulmonary node detection network and a pulmonary node sifting grading network; S300, obtaining pulmonary node temporal sequences of all patients in an image set and grading information marks corresponding to the pulmonary node sequences to construct a pulmonary node management database; S400, training a lung cancer diagnosis network based on a three-dimensional convolutional neural network and a long-short-term memory network. According to the method for grading and managing detection of the pulmonary nodes based on in-depth learning, the lung area segmentation network, the suspected pulmonary node detection network, the pulmonary node sifting grading network and the lung cancer diagnosis network are trained based on in-depth learning, the pulmonary nodes are accurately detected, and through the combination of subsequent tracking and visiting, more accurate diagnosis information and clinic strategies are obtained.

Description

technical field [0001] The present invention relates to the application of medical image diagnosis, database management, computer image processing, deep learning and other technologies in the screening and management of pulmonary nodules, especially a method and system for detecting, grading and managing pulmonary nodules based on deep learning. Background technique [0002] Lung cancer is one of the most important malignant tumors in my country. In the 2015 China Cancer Statistical Annual Report, there were 733,000 new cases of lung cancer (accounting for 17.1% of the total) and 610,000 deaths (accounting for 21.1% of the total). The rate and death rate take the first place, bringing huge losses to the health of the people and the country. At present, the 5-year survival rate of lung cancer is only 12% to 17%, and the 5-year survival rate of stage I lung cancer can reach more than 60%. Therefore, early detection of pulmonary nodules is the key to improving the survival rate ...

Claims

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Application Information

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IPC IPC(8): G06F19/00G06K9/46G06K9/62
CPCG16H50/20G06V10/464G06V2201/031G06F18/24G06F18/214
Inventor 周鹏张少霆任静青浩渺陈峥罗红兵胡仕北何长久
Owner SICHUAN CANCER HOSPITAL
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