An Ensemble Approach for Brain Tumor Segmentation and Synthesis

• URL: http://arxiv.org/abs/2411.17617v1
• Date: Tue, 26 Nov 2024 17:28:51 GMT
• Title: An Ensemble Approach for Brain Tumor Segmentation and Synthesis
• Authors: Juampablo E. Heras Rivera, Agamdeep S. Chopra, Tianyi Ren, Hitender Oswal, Yutong Pan, Zineb Sordo, Sophie Walters, William Henry, Hooman Mohammadi, Riley Olson, Fargol Rezayaraghi, Tyson Lam, Akshay Jaikanth, Pavan Kancharla, Jacob Ruzevick, Daniela Ushizima, Mehmet Kurt,
• Abstract summary: The integration of machine learning in magnetic resonance imaging (MRI) is proving to be incredibly effective. Deep learning models utilize multiple layers of processing to capture intricate details of complex data. We propose a deep learning framework that ensembles state-of-the-art architectures to achieve accurate segmentation.
• Score: 0.12777007405746044
• License:
• Abstract: 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.

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