New coronal pneumonia patient rehabilitation time prediction method and system based on deep learning

Abstract

The invention discloses a new coronavirus pneumonia patient rehabilitation time prediction method and system based on deep learning. The method comprises the steps: obtaining multi-day CT sequence images of a new coronavirus pneumonia patient, and carrying out the preprocessing of the multi-day CT sequence images; respectively inputting into a lung lobe segmentation model and a pneumonia segmentation model, and respectively extracting the lung lobe region area and the lesion region area of multiple days; calculating according to the ratio of the lesion area to the lung lobe area for multiple days to obtain a lesion area ratio value for multiple days; and fitting a Gaussian process model by using the lesion area proportion R of multiple days to predict the rehabilitation time of the novel coronavirus pneumonia patient. According to the lung lobe and pneumonia region segmentation method, the Densenet is used as the DeepLab V3 + framework and the 3D UNet framework of the backbone to segment the lung lobe and pneumonia region, the segmentation is quick and effective, the Gaussian process can accurately predict the rehabilitation time of the patient, and a reference is provided for medical resource allocation.

Description

technical field

[0001] The present invention relates to image semantic segmentation and machine learning technology, in particular to a method and system for predicting the recovery time of patients with new coronary pneumonia based on deep learning. Background technique

[0002] The rapid spread of the novel coronavirus pneumonia (COVID-19) disease has put unprecedented pressure on the healthcare systems of many countries. As of April 29, 2020, a total of 3,068,959 COVID-19 patients have been diagnosed worldwide. What's more, few experienced radiologists worldwide have the experience needed to diagnose or respond to COVID-19. In addition, due to the sudden and concentrated outbreak of the epidemic, it has brought enormous pressure to hospitals in various countries in a short period of time, resulting in a shortage of medical resources in a short period of time. In the case of shortage of medical resources, the case fatality rate can be as high as 9%. Timely detection, dia...

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