Related papers: ReFRM3D: A Radiomics-enhanced Fused Residual Multiparametric 3D Network with Multi-Scale Feature Fusion for Glioma Characterization

ReFRM3D: A Radiomics-enhanced Fused Residual Multiparametric 3D Network with Multi-Scale Feature Fusion for Glioma Characterization

URL: http://arxiv.org/abs/2512.22570v1
Date: Sat, 27 Dec 2025 12:12:53 GMT
Title: ReFRM3D: A Radiomics-enhanced Fused Residual Multiparametric 3D Network with Multi-Scale Feature Fusion for Glioma Characterization
Authors: Md. Abdur Rahman, Mohaimenul Azam Khan Raiaan, Arefin Ittesafun Abian, Yan Zhang, Mirjam Jonkman, Sami Azam,
Abstract summary: Gliomas are among the most aggressive cancers, characterized by high mortality rates and complex diagnostic processes.<n>Existing studies on glioma diagnosis and classification often describe issues such as high variability in imaging data.<n>We propose novel techniques utilizing multi-parametric MRI data to enhance tumor segmentation and classification efficiency.
Score: 4.546739588274135
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Gliomas are among the most aggressive cancers, characterized by high mortality rates and complex diagnostic processes. Existing studies on glioma diagnosis and classification often describe issues such as high variability in imaging data, inadequate optimization of computational resources, and inefficient segmentation and classification of gliomas. To address these challenges, we propose novel techniques utilizing multi-parametric MRI data to enhance tumor segmentation and classification efficiency. Our work introduces the first-ever radiomics-enhanced fused residual multiparametric 3D network (ReFRM3D) for brain tumor characterization, which is based on a 3D U-Net architecture and features multi-scale feature fusion, hybrid upsampling, and an extended residual skip mechanism. Additionally, we propose a multi-feature tumor marker-based classifier that leverages radiomic features extracted from the segmented regions. Experimental results demonstrate significant improvements in segmentation performance across the BraTS2019, BraTS2020, and BraTS2021 datasets, achieving high Dice Similarity Coefficients (DSC) of 94.04%, 92.68%, and 93.64% for whole tumor (WT), enhancing tumor (ET), and tumor core (TC) respectively in BraTS2019; 94.09%, 92.91%, and 93.84% in BraTS2020; and 93.70%, 90.36%, and 92.13% in BraTS2021.

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