Segmentation of glioma sub-regions based on EnnUnet in tumor treating fields
Year: 2025
Type: article
Abstract: Accurate segmentation of glioblastoma (GBM), including the whole tumor (WT), tumor core (TC), and enhancing tumor (ET), from multi-modal magnetic resonance images (MRI) is essential for precise Tumor Treating Fields (TTFields) simulation. This study aims to address the challenges of this segmentation task to improve the accuracy of TTFields simulation results. We propose enhanced nnUnet (EnnUnet), a novel framework for multi-modal MRI segmentation that enhances the robust and widely-used nnUnet architecture. This advanced architecture integrates three key innovations: (1) Generalized Multi-kernel Convolution blocks are incorporated to capture multi-scale features and long-range dependencies. (2) A dual attention mechanism is employed at skip connections to refine feature fusion. (3) A novel boundary and Top-K loss is implemented for boundary-based refinement and to focus the training process on hard-to-segment pixels. The effectiveness of each enhancement was systematically evaluated through an ablation study on the BraTS 2023 dataset. The final EnnUnet model achieved superior performance, with average Dice scores of 93.52%, 92.07%, and 87.60% for the WT, TC, and ET, respectively, consistently outperforming other state-of-the-art methods. Furthermore, TTFields simulations on real patient data demonstrated that our precise segmentations yield more realistic electric field distributions compared to simplified homogeneous tumor models. The proposed EnnUnet architecture showcases promising potential for highly accurate and robust glioma segmentation. It offers a more reliable foundation for computational modeling, which is essential for enhancing the precision of TTFields treatment planning and advancing personalized therapeutic strategies for GBM patients.
Authors: Liang Wang, Chunxiao Chen, Yueyue Xiao, Rongfang Gong, Jun Shen, Ming Lu
Institution: Nanjing University of Aeronautics and Astronautics
Language: English
Subfield: Neurology
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W4415249544