Spatial information using CRF for brain tumor segmentation

In this work, we proposed a method combined the fuzzy spatial correlation of voxels in the MRI images obtained from a 3D network using CRF with the slice information captured by an ordinary 2D network to focus on the brain tumor segmentation task. Considering the expensive devices required by 3D networks while 2D networks can loss the information in the channel direction which leads to many false positive predictions, the proposed one can be a favorable direction to get more accurate features of the brain tumor. We take MRI images with 4 modalities in BRATS2018 dataset as the input of the 3D CNN after reducing the resolution. The CRF is used to calculate the neighboring correlation after the CNN feature extractor and can generate the probability map. The 2D network takes 2D slices in 4 modalities from the MRI images as input and output the segmentation map. The 2D segmentation maps are joining to 3D in order and combined with the probability map to get the final result. Compared with the state-of-the-art and the baseline method with the average Dice less than 0.85, the proposed is time and memory saving with the average Dice nearly 0.88.