Method and system for pulmonary nodule detection, grading and management based on deep learning

Abstract

The invention discloses a method for detecting, grading and managing pulmonary nodules based on deep learning, which is characterized in that it includes the following steps: S100: collecting ultra-low-dose spiral CT thin-layer images of the chest, delineating the lung area in the CT image, and marking out All lung nodules in the lung area; S200: train the lung area segmentation network, suspected lung nodule detection network and lung nodule screening and grading network; S300: obtain the time series of lung nodules and their corresponding grading for all patients in the image set Information labeling, constructing a pulmonary nodule management database; S400: Training a lung cancer diagnosis network based on a three-dimensional convolutional neural network and a long-term short-term memory network. Based on deep learning, the present invention trains the lung region segmentation network, suspected pulmonary nodule detection network, pulmonary nodule screening and grading network and lung cancer diagnosis network, accurately detects pulmonary nodules, and combines follow-up follow-up to obtain more accurate diagnosis Information and Clinical Strategies.

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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