用风格迁移提升非洲低质MRI的脑肿瘤分割效果
Generative Style Transfer for MRI Image Segmentation: A Case of Glioma Segmentation in Sub-Saharan Africa
- 采用风格迁移增强非洲本地MRI数据，弥补数据不足
- 2D与3D模型在非洲数据上均达0.93的分割精度
- 适合资源有限地区医疗AI研究者参考
在撒哈拉以南非洲（SSA），较低质量的磁共振成像（MRI）技术对机器学习临床应用提出了挑战。本研究提出一种针对非洲人群的鲁棒深度学习脑肿瘤分割方法，采用三步策略：首先验证了非洲训练数据域偏移对模型性能无显著影响；其次对比3D与2D全分辨率模型，两者在300轮训练下五折交叉验证分数均为0.93；最后针对非洲验证集表现低于大规模BraTS glioma（GLI）数据集的问题，提出两种改进策略：使用GLI+SSA预训练的2D全分辨率模型对非洲病例进行微调，以及引入基于神经风格迁移的数据增强技术。该研究展示了在非洲独特医疗环境下提升脑肿瘤预测能力的可行性。
原文摘要 · Abstract (English)
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.
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