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

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 ...

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 (such as Follow-up of patients and follow-up management of pulmonary nodules) have few references, and adopt a simple classification diagnosis model without grading, its diagnostic effect cannot reach the diagnostic efficiency of clinicians, and it lacks guiding value for further clinical treatment of nodules

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