Tumor Segmentation in Brain MRI: U-Nets versus Feature Pyramid Network

Sourodip Ghosh, KC Santosh

Abstract: Manifestations of brain tumors can trigger various psychiatric symptoms. Brain tumor detection can efficiently solve or reduce chances of occurrences of diseases, such as Alzheimer's disease, dementia-based disorders, multiple sclerosis and bipolar disorder. In this paper, we propose a segmentation-based approach to detect brain tumors in MRI 1 1 Authors contributed equally to the manuscript.. We provide a comparative study between two different U-Net architectures (U-Net: baseline and U-Net: ResNeXt50 backbone) and a Feature Pyramid Network (FPN) that are trained/validated on the TCGA-LGG dataset of size 3, 929 images. U-Net architecture with ResNeXt50 backbone achieves the best Dice coefficient of 0.932, while baseline U-Net and FPN separately achieve Dice coefficients of 0.846 and 0.899, respectively. The results obtained from U-Net with ResNeXt50 backbone outperform previous works.