Abstract
Pediatric brain tumor segmentation presents unique challenges due to the rarity and heterogeneity of these malignancies, yet remains critical for clinical diagnosis and treatment planning. We propose an ensemble approach integrating nnU-Net, Swin UNETR, and HFF-Net for the BraTS-PED 2025 challenge. Our method incorporates three key extensions: adjustable initialization scales for optimal nnU-Net complexity control, transfer learning from BraTS 2021 pre-trained models to enhance Swin UNETR's generalization on pediatric dataset, and frequency domain decomposition for HFF-Net to separate low-frequency tissue contours from high-frequency texture details. Our final ensemble combines nnU-Net ($\gamma=0.7$), fine-tuned Swin UNETR, and HFF-Net, achieving Dice scores of 72.3% (ET), 95.6% (NET), 68.9% (CC), 89.5% (ED), 92.3% (TC), and 92.3% (WT), respectively.
Abstract (translated)
儿童脑肿瘤分割由于这些恶性肿瘤的罕见性和异质性而面临独特的挑战，但它对于临床诊断和治疗计划仍然至关重要。我们为BraTS-PED 2025挑战赛提出了一种集成方法，结合了nnU-Net、Swin UNETR和HFF-Net。我们的方法包含了三个关键扩展：可调初始化尺度以实现最佳的nnU-Net复杂度控制；从BraTS 2021预训练模型中进行迁移学习以增强Swin UNETR在儿童数据集上的泛化能力；以及通过频率域分解使HFF-Net能够将低频组织轮廓与高频纹理细节分离。最终集成方法包括调整参数为$\gamma=0.7$的nnU-Net、微调后的Swin UNETR和HFF-Net，分别取得了72.3%（增强肿瘤, ET）、95.6%（非增强肿瘤, NET）、68.9%（核心肿瘤, CC）、89.5%（全脑水肿, ED）、92.3%（全肿瘤, TC）和92.3%（全病变, WT）的Dice评分。
URL
https://arxiv.org/abs/2509.19353
