针对非洲低资源地区脑肿瘤分割难题，融合拓扑优化提升模型精度。
Topology-Driven Fusion of nnU-Net and MedNeXt for Accurate Brain Tumor Segmentation on Sub-Saharan Africa Dataset
- 引入拓扑精修模块，修复因拓扑误差导致的预测形变问题。
- 在SNFH/NETC/ET三项指标上NSD分别达0.810、0.829、0.895，优于基线模型。
- 适用于低场MRI图像质量差、医疗资源有限的地区脑肿瘤自动分割。
由于缺乏统一的影像采集协议、数据多样性高、普遍使用低场磁共振成像（MRI）设备以及医疗资源有限，低收入和中等收入国家（LMIC）的脑肿瘤自动分割面临挑战。作为BraTS Africa 2025挑战赛的一部分，我们对nnU-Net、MedNeXt及二者结合的先进分割模型进行了拓扑精修。鉴于BraTS-Africa数据集的影像质量较低，我们利用BraTS 2025成人胶质瘤术前任务（Task 1）数据预训练模型，并在BraTS-Africa数据集上进行微调。通过引入额外的拓扑精修模块，有效缓解了由拓扑错误引发的预测形变问题。该方法在周围非增强FLAIR高信号（SNFH）、非增强肿瘤核心（NETC）和增强肿瘤（ET）上的归一化表面距离（NSD）分别达到0.810、0.829和0.895。
原文摘要 · Abstract (English)
Accurate automatic brain tumor segmentation in Low and Middle-Income (LMIC) countries is challenging due to the lack of defined national imaging protocols, diverse imaging data, extensive use of low-field Magnetic Resonance Imaging (MRI) scanners and limited health-care resources. As part of the Brain Tumor Segmentation (BraTS) Africa 2025 Challenge, we applied topology refinement to the state-of-the-art segmentation models like nnU-Net, MedNeXt, and a combination of both. Since the BraTS-Africa dataset has low MRI image quality, we incorporated the BraTS 2025 challenge data of pre-treatment adult glioma (Task 1) to pre-train the segmentation model and use it to fine-tune on the BraTS-Africa dataset. We added an extra topology refinement module to address the issue of deformation in prediction that arose due to topological error. With the introduction of this module, we achieved a better Normalized Surface Distance (NSD) of 0.810, 0.829, and 0.895 on Surrounding Non-Enhancing FLAIR Hyperintensity (SNFH) , Non-Enhancing Tumor Core (NETC) and Enhancing tumor (ET).
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