Lung tumor sketching method based on 5G cloud radiotherapy private network

Abstract

The invention relates to a lung tumor sketching method based on a 5G cloud radiotherapy private network, and the method comprises the steps: collecting a preset number of CT chest images and T1 MRI chest images, transmitting the CT chest images and the T1 MRI chest images to an edge cloud iMEP through a private network gateway iGW, and obtaining a lung tumor segmentation neural network model; after the to-be-segmented image is sent to the edge cloud iMEP through the private network gateway iGW, inputting the lung tumor segmentation neural network model to obtain a lung tumor automatic segmentation result of the CT image, and uploading the lung tumor automatic segmentation result to the 5G cloud network; patient related information corresponding to the to-be-segmented image recorded by the common terminal is sent to the 5G cloud network through a private network gateway iGW; and logging in the 5G cloud network to fuse the lung tumor automatic segmentation result and the corresponding patient related information, and carrying out post-processing and edge detection on the lung tumor automatic segmentation result to obtain a lung tumor sketching result.

Description

technical field [0001] The invention relates to the technical field of 5G data application, in particular to a lung tumor delineation method based on a 5G cloud radiotherapy private network. Background technique [0002] In the clinical diagnosis of radiotherapy and other departments, it is extremely important to accurately segment lung tumors. The business data obtained from the examination, outline, and segmentation of lung tumors are data that need to be kept confidential in the hospital. When other hospitals or other experts are required to conduct remote consultations, these data can be uploaded through wireless networks or 5G public networks. To the cloud platform, various hospitals and experts also access the cloud platform to download and view by connecting to the wireless network or 5G public network, realizing the convenience of modern medical informationization. [0003] However, the current medical information system generally has the following three problems: ...

AI Technical Summary

Problems solved by technology

First, the information construction is messy and the maintenance cost is very high

It is mainly manifested in the serious phenomenon of isolated islands of information and frequent occurrence of information security accidents; the use of WIFI network is poor, which seriously affects the use of business systems; the cost of self-built IDC in hospitals is high, the level is low, and operation and maintenance are difficult

[0004] Second, there is a lack of isolation security between businesses

It is necessary to solve the integration problem of the personal business of individual users accessing the Internet and the private line business of the hospital's own data center; realize the data out of the hospital, and solve the security concerns of the hospital's business on the cloud

[0005] Third, the hospital's own business SLA guarantee is poor

[0006] In the face of the above problems, neither traditional methods nor 5G public networks can completely overcome them. It is necessary to build a new DICT infrastructure based on the 5G medical industry private network and integrate AICEO's hard-core capabilities, so as to realize the purpose of 5G to accelerate the informatization transformation of the medical industry

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