Brain tumour segmentation in fused MRI-PET images with permutate U-Net framework

Brain tumor segmentation from MRI's and PET has always been a challenging and time-consuming phase for radiologists, due to low sensitivity boundary region pixels in this image modality. Deep learning-based image segmentation is the hot research topic in recent days. Among all other deep learning models, U-Net-based variants are the most used models to segment medical images with respect to different modalities. In this paper, a Permutate version of the U-Net architecture was designed that precisely and automatically detects the boundaries of the tumour area and segments tumour regions from the fused image. There are two stages to the proposed work. In the first stage Principal component analysis (PCA) is used to fuse the MRI-PET images to enhance the fused image's quality and improved interpretation. Later, a Permutate U-Net architecture is employed to precisely segment tumour region from the fused image. Further designed model performance is assessed using Dice Coefficient, intersection over union score (IoU) and accuracy with brain tumour segmentation challenge BraTS datasets of 2015, 2020 and 2021. Our proposed method demonstrates promising results that are superior to existing deep learning model and comparatively higher than the existing methods.