Meningioma rapid segmentation qualitative method based on deep neural network

Abstract

The invention relates to the field of meningioma level judgment before an operation, and discloses a meningioma rapid segmentation qualitative method based on a deep neural network, and the method comprises the following steps: preparing a magnetic resonance brain image; establishing a meningioma segmentation model, and screening out an effective image containing a meningioma region from the magnetic resonance brain image through the meningioma segmentation model; and establishing a meningioma grading model, carrying out grading detection on the effective image through the meningioma grading model, and outputting a meningioma grading detection result. By means of the method, only a series of magnetic resonance brain images generated after scanning of a certain patient need to be input intothe network, the grading result of meningioma of the patient is rapidly given after comprehensive analysis and calculation of all the magnetic resonance brain images, the purpose of assisting a doctor in diagnosis is achieved, the whole process is automatically conducted, a large amount of repeated work of doctors is reduced, time is saved, and a patient can be treated more quickly.

Description

technical field [0001] The present invention relates to the field of meningioma grade judgment before operation, in particular to a method for fast segmentation and qualitative meningioma based on deep neural network. Background technique [0002] Meningioma originates from the meninges and the derivatives of the meningeal space, and is the second most common intracranial tumor, accounting for 13% to 26% of intracranial tumors, and its incidence has been on the rise in recent years. According to the 2016 WHO classification of tumors of the central nervous system, meningiomas are divided into three grades. Most meningiomas are classified as WHO grade I lesions, that is, benign lesions with slow growth and less recurrence after surgery. A small number of lesions are classified as WHO grade II or III lesions according to local invasiveness and atypical cell characteristics, that is, malignant lesions. Patients may suffer from blindness, hemiplegia, epilepsy and other symptoms. ...

AI Technical Summary

Problems solved by technology

[0004] Although most hospitals have MRI scanning machines, what is more important is the diagnosis by professional doctors. For relatively remote rural hospitals, it is difficult to obtain clinical opinions from experienced doctors in a timely manner.

At the same time, due to the very small intervals of tomographic scans, there are usually 200-300 brain magnetic tomographic scan slices of a patient, which is a huge number. It is a huge workload, time-consuming, and efficient to rely entirely on doctors' manual image reading and diagnosis. lower

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