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.
Multimodal nuclear magnetic resonance image segmentation method for glioblastoma