A deep-learning framework for brain tumor segmentation via three-dimensional mass-preserving geometric transformation.
Authors
Affiliations (5)
Affiliations (5)
- Department of Mathematics, National Taiwan Normal University, Taipei, 116, Taiwan. [email protected].
- Department of Applied Mathematics, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan.
- Shanghai Institute for Mathematics and Interdisciplinary Sciences, Shanghai, China.
- School of Mathematics, Southeast University, Nanjing, China.
- Yau Mathematical Sciences Center, Tsinghua University, Beijing, China.
Abstract
This article presents a robust and efficient framework for brain tumor segmentation based on deep learning. We introduce a novel three-dimensional (3D) mass-preserving geometric transformation (MPGT) that employs a homotopy method to transform irregular brain magnetic resonance (MR) images into standardized solid cubes. This transformation preserves local mass ratios while maintaining global structural integrity, providing a structured input for deep learning models. Furthermore, we propose a modified two-phase segmentation strategy to minimize inference time and a postprocessing technique to enhance lesion-wise performance. Extensive validation on the Brain Tumor Segmentation (BraTS) Challenge 2023 dataset demonstrates that our method, when integrated with nnU-Net, achieves competitive Dice scores of 0.9282 (Whole Tumor), 0.8812 (Tumor Core), and 0.8527 (Enhanced Tumor). These results are superior to or comparable with top-ranking competition entries.