To improve the accuracy of segmenting the tumor sub-regions in glioblastoma multiforme (GBM) multi-modal magnetic resonance (MR) images

a GBM multi-modal MR images automatic segmentation algorithm is proposed by using multi-class convolution neural network (CNN). Firstly

after 98% winsorization and registration for the GBM multi-modal MR images

the bias field was corrected by using the N4ITK method. Secondly

a multi-class CNN model mainly consisting of four convolutional layers

two pooling layers and two fully connected layers was constructed; the GBM multi-modal MR images were pre-segmented after training

and voxels were classified into five different labels.Finally

all false positive regions smaller than 200 voxels were removed

and the final segmentation results were obtained by median filtering. The Dice similarity coefficient DSC

positive predictive value PPV and average Hausdorff distance AHD were adopted as the evaluation index

and the DSC

PPV as well as AHD were 0.889±0.087

0.859±0.127 and 1.923 for segmenting the entire tumor tissues in F-C-GBM dataset by the proposed algorithm

respectively. Results indicate that the proposed method can effectively improve the performance in the segmentation of the GBM multi-modal MR images and may be expected to have clinical application prospects.