Cascade multiscale residual attention CNNs with adaptive ROI for automatic brain tumor segmentation

A brain tumor is one of the fatal cancer types which causes abnormal growth of brain cells. Earlier diagnosis of a brain tumor can play a vital role in its treatment; however, manual segmentation of the brain tumors from MRI images is a laborious and time-consuming task. Therefore, several automatic segmentation techniques have been proposed, but high inter-and intra-tumor variations in shape and texture make accurate segmentation of enhanced and core tumor regions challenging. The demand for a highly accurate and robust segmentation method persists; therefore, in this paper, we propose a novel fully automatic technique for brain tumor regions segmentation by using multiscale residual attention-UNet (MRA-UNet). MRA-UNet uses three consecutive slices as input to preserve the sequential information and employs multiscale learning in a cascade fashion, enabling it to exploit the adaptive region of interest scheme to segment enhanced and core tumor regions accurately. The study also investigates the application of postprocessing techniques (i.e., conditional random field and test time augmentation), which further helps to improve the overall performance. The proposed method has been rigorously evaluated on the most extensive publicly available datasets, i.e., BraTS2017, BraTS2019, and BraTS2020. Our approach achieved state-of-the-art results on the BraTS2020 dataset with the average dice score of 90.18%, 87.22%, and 86.74% for the whole tumor, tumor core, and enhanced tumor regions, respectively. A significant improvement has been demonstrated for the core and enhancing tumor region segmentation showing the proposed method's evident effectiveness.