Multimodal nuclear magnetic resonance image segmentation method for glioblastoma

Abstract

The invention provides a multimodal nuclear magnetic resonance image segmentation method for glioblastoma. The segmentation strategy combining the random forest method and the regional growth method is employed, the result of regional growth segmentation of a glioma multimodal magnetic resonance image is replaced with the corresponding random forest segmentation result with low confidence, retraining data is generated to re-train a random forest model, fine segmentation of the glioma multimodal magnetic resonance image is carried out, and a brain MRI image is segmented into a normal brain tissue area, a necrotic area, an active tumor area, a T1 abnormal area and a FLAIR abnormal area. The method is advantaged in that through fine segmentation and positioning of the glioblastoma, doctors are assisted in diagnosis and other treatment tasks, accurate positioning of the glioblastoma and more accurate fine segmentation of different tumor sub areas are carried out, the doctors are facilitated to diagnose the glioblastoma more quickly and accurately, and an accurate treatment scheme is made.

Description

technical field [0001] The invention relates to the fields of digital medical image analysis and intelligent health management, in particular to a multimodal nuclear magnetic resonance image segmentation method for glioblastoma. Background technique [0002] Glioma is the most common primary brain tumor, more than half of which are the most malignant glioblastoma (Glioblastoma Multiforme, GBM). For GBM, no matter its form is benign or malignant, it will increase intracranial pressure, compress brain tissue, cause damage to the central nervous system, and endanger the life of the patient. The location and quantitative calculation of GBM lesion tissue (such as tumor volume, etc.) are crucial for the diagnosis, surgical planning, and postoperative analysis of GBM. In clinical practice, radiologists usually manually segment tumors from multimodal magnetic resonance images, which is tedious and time-consuming. Automatically segmenting GBM tumors can not only free doctors from i...

AI Technical Summary

Problems solved by technology

However, through the analysis of the existing GBM multimodal magnetic resonance image segmentation methods, it is found that although the above studies have achieved certain research results, they all focus on searching and segmenting the entire tumor area, and do not further subdivide the entire tumor into tumors. sub-region

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