标题： 基于生成风格迁移的MRI图像分割：撒哈拉以南非洲胶质瘤分割案例 显示英文标题

标题： Generative Style Transfer for MRI Image Segmentation: A Case of Glioma Segmentation in Sub-Saharan Africa

摘要： 在撒哈拉以南非洲（SSA），使用低质量的磁共振成像（MRI）技术引发了关于机器学习方法在临床任务中适用性的疑问。本研究旨在通过一种三步方法，为SSA人群提供一种稳健的基于深度学习的脑肿瘤分割（BraTS）方法。首先，检查了SSA训练数据的领域偏移对模型效果的影响，结果表明没有显著影响。其次，使用nnU-Net框架对3D和2D全分辨率模型进行了比较分析，结果显示两者在训练300个周期后达到五折交叉验证分数0.93，性能相似。最后，针对在SSA验证中观察到的性能差距，与相对较大的BraTS胶质瘤（GLI）验证集相比，提出了两种策略：使用GLI+SSA最佳预训练2D全分辨率模型在300个周期内微调SSA病例，并为SSA病例引入一种基于神经风格迁移的数据增强技术。这项研究突显了在SSA独特的医疗环境中提高脑肿瘤预测的潜力。 显示英文摘要

摘要： In Sub-Saharan Africa (SSA), the utilization of lower-quality Magnetic Resonance Imaging (MRI) technology raises questions about the applicability of machine learning methods for clinical tasks. This study aims to provide a robust deep learning-based brain tumor segmentation (BraTS) method tailored for the SSA population using a threefold approach. Firstly, the impact of domain shift from the SSA training data on model efficacy was examined, revealing no significant effect. Secondly, a comparative analysis of 3D and 2D full-resolution models using the nnU-Net framework indicates similar performance of both the models trained for 300 epochs achieving a five-fold cross-validation score of 0.93. Lastly, addressing the performance gap observed in SSA validation as opposed to the relatively larger BraTS glioma (GLI) validation set, two strategies are proposed: fine-tuning SSA cases using the GLI+SSA best-pretrained 2D fullres model at 300 epochs, and introducing a novel neural style transfer-based data augmentation technique for the SSA cases. This investigation underscores the potential of enhancing brain tumor prediction within SSA's unique healthcare landscape.