对比多种数据增强方法在视网膜OCT分割中的效果差异
Comparative Analysis of Data Augmentation for Retinal OCT Biomarker Segmentation
- 系统测试多种数据增强策略对视网膜层边界和积液分割的影响
- 增强效果随数据量少而显著提升，尤其在标注数据稀缺时
- 建议根据数据特征和模型架构选择适配的增强方案
数据增强在解决视网膜光学相干断层扫描（OCT）深度学习应用中专家标注数据有限的问题中起着关键作用。本研究全面考察了不同数据增强技术对视网膜层边界与液体分割的影响。结果表明，其有效性显著依赖于数据集特征及可用标注数据量。尽管增强的好处并非普遍适用——在标注数据稀少的情况下尤为明显，尤其是基于变换的方法——但这些发现强调了采用战略性数据增强方法的必要性。值得注意的是，数据增强的效果因数据集特性而异。研究强调需采取细致的方法，综合考虑数据集特征、标注数据量及模型架构的选择。
原文摘要 · Abstract (English)
Data augmentation plays a crucial role in addressing the challenge of limited expert-annotated datasets in deep learning applications for retinal Optical Coherence Tomography (OCT) scans. This work exhaustively investigates the impact of various data augmentation techniques on retinal layer boundary and fluid segmentation. Our results reveal that their effectiveness significantly varies based on the dataset's characteristics and the amount of available labeled data. While the benefits of augmentation are not uniform - being more pronounced in scenarios with scarce data, particularly for transformation-based methods - the findings highlight the necessity of a strategic approach to data augmentation. It is essential to note that the effectiveness of data augmentation varies significantly depending on the characteristics of the dataset. The findings emphasize the need for a nuanced approach, considering factors like dataset characteristics, the amount of labelled data, and the choice of model architecture.
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