Hybrid Multihead Attentive Unet-3D for Brain Tumor Segmentation

TL;DR

This paper introduces a novel Hybrid Multihead Attentive U-Net architecture that combines attention mechanisms with the U-Net model to improve the accuracy of brain tumor segmentation, addressing the complex morphology of tumors.

Contribution

The paper presents a new hybrid architecture that integrates multihead attention with U-Net for enhanced brain tumor segmentation accuracy.

Findings

Outperforms state-of-the-art models on BraTS 2020 dataset

Improves segmentation boundary accuracy

Enhances focus on informative regions

Abstract

Brain tumor segmentation is a critical task in medical image analysis, aiding in the diagnosis and treatment planning of brain tumor patients. The importance of automated and accurate brain tumor segmentation cannot be overstated. It enables medical professionals to precisely delineate tumor regions, assess tumor growth or regression, and plan targeted treatments. Various deep learning-based techniques proposed in the literature have made significant progress in this field, however, they still face limitations in terms of accuracy due to the complex and variable nature of brain tumor morphology. In this research paper, we propose a novel Hybrid Multihead Attentive U-Net architecture, to address the challenges in accurate brain tumor segmentation, and to capture complex spatial relationships and subtle tumor boundaries. The U-Net architecture has proven effective in capturing contextual information and feature representations, while attention mechanisms enhance the model's ability to focus on informative regions and refine the segmentation boundaries. By integrating these two components, our proposed architecture improves accuracy in brain tumor segmentation. We test our proposed model on the BraTS 2020 benchmark dataset and compare its performance with the state-of-the-art well-known SegNet, FCN-8s, and Dense121 U-Net architectures. The results show that our proposed model outperforms the others in terms of the evaluated performance metrics.