Purpose

Data augmentation is a common technique to overcome the lack of large annotated databases, a usual situation when applying deep learning to medical imaging problems. Nevertheless, there is no consensus on which transformations to apply for a particular field. This work aims at identifying the effect of different transformations on polyp segmentation using deep learning.

Methods

A set of transformations and ranges have been selected, considering image-based (width and height shift, rotation, shear, zooming, horizontal and vertical flip and elastic deformation), pixel-based (changes in brightness and contrast) and application-based (specular lights and blurry frames) transformations. A model has been trained under the same conditions without data augmentation transformations (baseline) and for each of the transformation and ranges, using CVC-EndoSceneStill and Kvasir-SEG, independently. Statistical analysis is performed to compare the baseline performance against results of each range of each transformation on the same test set for each dataset.

Results

This basic method identifies the most adequate transformations for each dataset. For CVC-EndoSceneStill, changes in brightness and contrast significantly improve the model performance. On the contrary, Kvasir-SEG benefits to a greater extent from the image-based transformations, especially rotation and shear. Augmentation with synthetic specular lights also improves the performance.

Conclusion

Despite being infrequently used, pixel-based transformations show a great potential to improve polyp segmentation in CVC-EndoSceneStill. On the other hand, image-based transformations are more suitable for Kvasir-SEG. Problem-based transformations behave similarly in both datasets. Polyp area, brightness and contrast of the dataset have an influence on these differences.

中文翻译：

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揭示息肉分割中数据增强变换的效果

 目的

数据增强是克服大型注释数据库缺乏的常用技术，这是将深度学习应用于医学成像问题时的常见情况。然而，对于哪些转换适用于特定领域尚未达成共识。这项工作旨在利用深度学习识别不同变换对息肉分割的影响。

 方法

考虑到基于图像（宽度和高度移位、旋转、剪切、缩放、水平和垂直翻转以及弹性变形）、基于像素（亮度和对比度的变化）和基于应用程序（亮度和对比度的变化），选择了一组变换和范围。镜面光和模糊帧）转换。模型已在相同条件下进行训练，无需数据增强转换（基线），并且对于每个转换和范围，均独立使用 CVC-EndoSceneStill 和 Kvasir-SEG。执行统计分析以将基线性能与每个数据集的同一测试集上每个转换的每个范围的结果进行比较。

 结果

这种基本方法确定了每个数据集的最适当的转换。对于 CVC-EndoSceneStill，亮度和对比度的变化显着提高了模型性能。相反，Kvasir-SEG 在更大程度上受益于基于图像的变换，尤其是旋转和剪切。使用合成镜面光增强也可以提高性能。

 结论

尽管不经常使用，但基于像素的变换显示出改善 CVC-EndoSceneStill 中息肉分割的巨大潜力。另一方面，基于图像的变换更适合 Kvasir-SEG。基于问题的转换在两个数据集中的行为相似。数据集的息肉面积、亮度和对比度对这些差异有影响。