Arouse-Net: Enhancing Glioblastoma Segmentation in Multi-Parametric MRI with a Custom 3D Convolutional Neural Network and Attention Mechanism

Haiyang Li, Xiaozhi Qi (), Ying Hu and Jianwei Zhang
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Haiyang Li: Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Xiaozhi Qi: Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Ying Hu: Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Jianwei Zhang: Department of Informatics, University of Hamburg, 22527 Hamburg, Germany

Mathematics, 2025, vol. 13, issue 1, 1-16

Abstract: Glioblastoma, a highly aggressive brain tumor, is challenging to diagnose and treat due to its variable appearance and invasiveness. Traditional segmentation methods are often limited by inter-observer variability and the lack of annotated datasets. Addressing these challenges, this study introduces Arouse-Net, a 3D convolutional neural network that enhances feature extraction through dilated convolutions, improving tumor margin delineation. Our approach includes an attention mechanism to focus on edge features, essential for precise glioblastoma segmentation. The model’s performance is benchmarked against the state-of-the-art BRATS test dataset, demonstrating superior results with an over eight times faster processing speed. The integration of multi-modal MRI data and the novel evaluation protocol developed for this study offer a robust framework for medical image segmentation, particularly useful for clinical scenarios where annotated datasets are limited. The findings of this research not only advance the field of medical image analysis but also provide a foundation for future work in the development of automated segmentation tools for brain tumors.

Keywords: brain tumor segmentation; deep neural networks; medical imaging; BRATS (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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