Utilizing Attention, Linked Blocks, And Pyramid Pooling To Propel Brain Tumor Segmentation In 3D

Pooja Ravi, Srijarko Roy, Indira Dutta

Keywords: Computer Vision, Deep Learning, 3D Semantic Segmentation, Medical Imaging

Abstract: We present an approach to detect and segment tumorous regions of the brain by establishing three varied segmentation architectures for multiclass semantic segmentation along with data specific customizations like residual blocks, soft attention mechanism, pyramid pooling, linked architecture and 3D compatibility to work with 3D brain MRI images. The proposed segmentation architectures namely, Attention Residual UNET 3D also referred to as AR-UNET 3D, LinkNet 3D and PSPNet 3D, segment the MRI images and succeed in isolating three classes of tumors. By assigning pixel probabilities, each of these models differentiates between pixels belonging to tumorous and non-tumorous regions of the brain. By experimenting and observing the performance of each of the three architectures using metrics like Dice loss and Dice score, on the BraTS2020 dataset, we successfully establish quality results.

One-sentence Summary: We propose three novel 3D semantic segmentation architectures which utilize soft attention mechanism, linked networks and pyramid pooling for segmenting brain tumors in 3D.