Glioma, a common and deadly brain tumor, requires early diagnosis for improved prognosis. However, low-quality Magnetic Resonance Imaging (MRI) technology in Sub-Saharan Africa (SSA) hinders accurate diagnosis. This paper presents our work in the BraTS Challenge on SSA Adult Glioma. We adopt the model from the BraTS-GLI 2021 winning solution and utilize it with three training strategies: (1) initially training on the BraTS-GLI 2021 dataset with fine-tuning on the BraTS-Africa dataset, (2) training solely on the BraTS-Africa dataset, and (3) training solely on the BraTS-Africa dataset with 2x super-resolution enhancement. Results show that initial training on the BraTS-GLI 2021 dataset followed by fine-tuning on the BraTS-Africa dataset has yielded the best results. This suggests the importance of high-quality datasets in providing prior knowledge during training. Our top-performing model achieves Dice scores of 0.882, 0.840, and 0.926, and Hausdorff Distance (95%) scores of 15.324, 37.518, and 13.971 for enhancing tumor, tumor core, and whole tumor, respectively, in the validation phase. In the final phase of the competition, our approach successfully secured second place overall, reflecting the strength and effectiveness of our model and training strategies. Our approach provides insights into improving glioma diagnosis in SSA, showing the potential of deep learning in resource-limited settings and the importance of transfer learning from high-quality datasets.

翻译：胶质瘤是一种常见且致命的脑肿瘤，早期诊断对于改善预后至关重要。然而，撒哈拉以南非洲地区低质量的磁共振成像技术阻碍了准确诊断。本文介绍了我们在BraTS挑战赛SSA成人胶质瘤赛道上的工作。我们采用了BraTS-GLI 2021获胜解决方案的模型，并运用了三种训练策略：(1) 先在BraTS-GLI 2021数据集上训练，然后在BraTS-Africa数据集上进行微调；(2) 仅在BraTS-Africa数据集上训练；(3) 仅在经过2倍超分辨率增强的BraTS-Africa数据集上训练。结果表明，先在BraTS-GLI 2021数据集上训练，再在BraTS-Africa数据集上进行微调的策略取得了最佳效果。这揭示了高质量数据集在训练过程中提供先验知识的重要性。我们表现最佳的模型在验证阶段，对于增强肿瘤、肿瘤核心和整个肿瘤区域，分别取得了0.882、0.840和0.926的Dice分数，以及15.324、37.518和13.971的豪斯多夫距离（95%）分数。在比赛的最终阶段，我们的方法成功获得了总排名第二，体现了我们模型和训练策略的强度与有效性。我们的方法为改善撒哈拉以南非洲地区的胶质瘤诊断提供了见解，展示了深度学习在资源有限环境下的潜力，以及从高质量数据集进行迁移学习的重要性。