Brain Tumor Segmentation in MRI Images with 3D U-Net and Contextual Transformer

TL;DR

This paper introduces an advanced 3D U-Net model integrated with a Context Transformer for improved brain tumor segmentation in MRI images, demonstrating superior accuracy over existing methods.

Contribution

The study develops a novel 3D U-Net with integrated Context Transformer to enhance contextual feature extraction in brain tumor segmentation.

Findings

Achieved Dice scores of 82.0%, 81.5%, and 89.0% on BraTS2019 for different tumor regions.

Outperformed current state-of-the-art segmentation methods.

Enhanced capture of tumor location, size, and boundaries.

Abstract

This research presents an enhanced approach for precise segmentation of brain tumor masses in magnetic resonance imaging (MRI) using an advanced 3D-UNet model combined with a Context Transformer (CoT). By architectural expansion CoT, the proposed model extends its architecture to a 3D format, integrates it smoothly with the base model to utilize the complex contextual information found in MRI scans, emphasizing how elements rely on each other across an extended spatial range. The proposed model synchronizes tumor mass characteristics from CoT, mutually reinforcing feature extraction, facilitating the precise capture of detailed tumor mass structures, including location, size, and boundaries. Several experimental results present the outstanding segmentation performance of the proposed method in comparison to current state-of-the-art approaches, achieving Dice score of 82.0%, 81.5%, 89.0% for Enhancing Tumor, Tumor Core and Whole Tumor, respectively, on BraTS2019.