Pulmonary fibrosis detection and severity evaluation method and system based on deep learning

Abstract

The invention provides a pulmonary fibrosis detection and severity evaluation method based on deep learning, and the method comprises the steps: S1, preprocessing chest CT sequence images of a plurality of pulmonary fibrosis patients, and obtaining a plurality of first CT images; S2, extracting and labeling a plurality of first CT images to generate a training set and a verification set; S3, pre-training a first deep convolutional neural network model and a second deep convolutional neural network model through the training set and the verification set; S4, inputting the CT image sequence of the patient to be detected into the trained first and second deep convolutional neural network models, and identifying a lung region and a pulmonary fibrosis focus region contained in each CT image inthe CT image sequence of the patient; calculating to obtain the proportion of the pulmonary fibrosis focus of the patient in the lung; S5, marking pulmonary fibrosis staging based on the proportion; and S6, grading the pulmonary fibrosis severity of the patient based on the detection result of the physiological parameters. The invention further comprises a pulmonary fibrosis detection and severityevaluation system based on deep learning.

Description

technical field [0001] The invention relates to the field of medical image analysis, in particular to a method and system for detecting and evaluating pulmonary fibrosis based on deep learning. Background technique [0002] Pulmonary fibrosis (PF) is a common outcome of various lung diseases, and its main manifestation is scarring of lung tissue. If it is widely involved, it will lead to reduced lung volume and significant decline in lung function, seriously affecting the quality of life of patients. In particular, idiopathic interstitial pneumonia (idiopathic pulmonary fibrosis, IPF) is the most typical representative, and its pathology and / or imaging shows a chronic progressive lung disease with usual interstitial pneumonia. The etiology of IPF is unknown, the prognosis is extremely poor, and the average survival period after diagnosis is only 3-5 years. At present, IPF believes that the survival period of individual patients varies greatly. Some patients survive stably f...

AI Technical Summary

Problems solved by technology

However, the existing methods have a low recognition rate for pulmonary fibrosis, and the recognition speed is slow

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