An Ensemble Approach for Brain Tumor Segmentation and Synthesis

• Main Authors: Rivera, Juampablo E. Heras, Chopra, Agamdeep S, Ren, Tianyi, Oswal, Hitender, Pan, Yutong, Sordo, Zineb, Walters, Sophie, Henry, William, Mohammadi, Hooman, Olson, Riley, Rezayaraghi, Fargol, Lam, Tyson, Jaikanth, Akshay, Kancharla, Pavan, Ruzevick, Jacob, Ushizima, Daniela, Kurt, Mehmet
• Format: Journal Article
• Language: English
• Published: 26-11-2024
• Subjects: Computer Science - Computer Vision and Pattern Recognition
• DOI: 10.48550/arxiv.2411.17617

The integration of machine learning in magnetic resonance imaging (MRI),
specifically in neuroimaging, is proving to be incredibly effective, leading to
better diagnostic accuracy, accelerated image analysis, and data-driven
insights, which can potentially transform patient care. Deep learning models
utilize multiple layers of processing to capture intricate details of complex
data, which can then be used on a variety of tasks, including brain tumor
classification, segmentation, image synthesis, and registration. Previous
research demonstrates high accuracy in tumor segmentation using various model
architectures, including nn-UNet and Swin-UNet. U-Mamba, which uses state space
modeling, also achieves high accuracy in medical image segmentation. To
leverage these models, we propose a deep learning framework that ensembles
these state-of-the-art architectures to achieve accurate segmentation and
produce finely synthesized images.