Automatic Segmentation for Glioblastoma Multiforme Using Multimodal MR Images and Multiple Features

Glioblastoma multiforme(GBM)is the most malignant glioma,and localization and quantification of diseased tissues is crucial for tumor diagnosis and treatment planning.To improve the accuracy of GBM automatic segmentation,a novel GBM automatic segmentation method is proposed based on multimodal MR images and multiple features.Firstly,after image registration and bias field correction,multiple low-level features of each voxel were extracted from the GBM multimodal magnetic resonance(MR)images,and a random forest(RF)model was constructed for coarse segmentation according to the features information.Secondly,the corresponding coarse segmentation results with low confidence were replaced by the results of multiple seeds three dimensional region growing segmentation for GBM multimodal MR images,and the RF model was retrained using the generated training data,then fine segmentation of GBM multimodal MR images was implemented.Finally,considering the prior knowledge of the GBM tumors anatomical structures,the final results were achieved after threshold segmentation and median filtering of the fine segmentation results.The proposed algorithm adopts the average Dice similarity coefficient,Hausdorff distance and sensitivity as the evaluation index,the average Dice similarity coefficient,Hausdorff distance and sensitivity of the entire tumor in the GBM-nih-zcmu database are 0.879,6.232 and 0.863,respectively,which effectively improve the accuracy of automatic segmentation of GBM multi-modal MR images and meet the accuracy requirements of clinical applications.