Mutltimodal MRI Brain Tumor Segmentation using 3D Attention UNet with Dense Encoder Blocks and Residual Decoder Blocks

Abstract

Medical image segmentation is essential for disease diagnosis and for support- ing medical decision systems. Automatic segmentation of brain tumors from Magnetic Resonance Imaging (MRI) is crucial for treatment planning and timely diagnosis. Due to the enormous amount of data that MRI provides as well as the variability in the location and size of the tumor, automatic seg- mentation is a difficult process. Consequently, a current outstanding problem in the field of deep learning-based medical image analysis is the development of an accurate and trustworthy way to separate the tumorous region from healthy tissues. In this paper, we propose a novel 3D Attention U-Net with dense encoder blocks and residual decoder blocks, which combines the bene- fits of both DenseNet and ResNet. Dense blocks with transition layers help to strengthen feature propagation, reduce vanishing gradient, and increase the receptive field. Because each layer receives feature maps from all previous layers, the network can be made thinner and more compact. To make predic- tions, it considers both low-level and high-level features at the same time. In addition, shortcut connections between the residual network are used to pre- serve low-level features at each level. As part of the proposed architecture, skip connections between dense and residual blocks are utilized along with an attention layer to speed up the training process. The proposed architecture was trained and validated using BraTS 2020 dataset, it showed promising results with dice scores of 0.866, 0.889, and 0.828 for the tumor core (TC), whole tumor (WT), and enhancing tumor (ET), respectively. In compar- ison to the original 3D U-Net, our approach performs better. According to the findings of our experiment, our approach is a competitive automatic brain tumor segmentation method when compared to some state-of-the-art techniques.