Artifact Aware Deep Learning with Diffuse Model for MRI Brain Tumor Image Segmentation

Abstract

Brain tumor segmentation in MRI images is crucial for clinical diagnosis and treatment planning but those scans are usually affected by imaging artifacts which decrease the quality of data and hamper segmentation performance. To address these challenges, this study proposed a unique framework that seamlessly combines artifact correction with segmentation of tumors. The framework features a data preparation module which is able to prepare realistic artifact-contaminated and artifact-free MRI image pairs that have been used for training. It also includes a diffuse model which acts on MRI images and removes the artifacts thus giving high-quality inputs for segmentation. In ad-dition, a modified 3D Convolutional Neural Network (CNN) architecture which integrates attention blocks and squeeze-and-excitation (SE) layers is used to segment the tumor sub-regions, including the enhancing tumor (ET), tumor core (TC), and whole tumor (WT). The framework was evaluated with artifact-corrupted data and clean data and achieved better results regarding the generation of artifact-free data and stable segmentation than the other baseline methods. This method emphasizes the magnitude of imaging artifacts on MRI-based segmentation and facilitates improvement in the clinical workflows. The code is available at https://github.com/Rahman3175/MMR