论文标题
切割数据增强：医学图像分割的新技术
Cutting-Splicing data augmentation: A novel technology for medical image segmentation
论文作者
论文摘要
背景：与自然图像相比，医学图像更难获取和注释，这导致数据增强技术经常用于医学图像分割任务。医学分割中使用的大多数数据增强技术最初是在自然图像上开发的，并且没有考虑到医疗图像的整体布局是标准且固定的特征。方法：根据医学图像的特征，我们开发了切割数据增强（CS-DA）方法，这是一种用于医学图像分割的新型数据增强技术。 CS-DA通过将不同原始医疗图像切割为新图像切割的不同位置组件来增加数据集。医学图像的特征导致新图像具有与原始图像相同的布局和相似的外观。与经典数据增强技术相比，CS-DA更简单，更健壮。此外，CS-DA不会在新创建的图像中引入任何噪音或虚假信息。结果：探索CS-DA的性质，在八个不同的数据集上进行了许多实验。在较小样本量的训练数据集上，CS-DA可以有效地提高分割模型的性能。当CS-DA与经典数据增强技术一起使用时，可以进一步改进分割模型的性能，并且比CS-DA和经典数据扩展的性能要好得多。我们还探讨了组件数量，切割线的位置以及剪接方法对CS-DA性能的影响。结论：CS-DA在实验中的出色性能已经确认了CS-DA的有效性，并为小样本分割任务提供了新的数据增强想法。
Background: Medical images are more difficult to acquire and annotate than natural images, which results in data augmentation technologies often being used in medical image segmentation tasks. Most data augmentation technologies used in medical segmentation were originally developed on natural images and do not take into account the characteristic that the overall layout of medical images is standard and fixed. Methods: Based on the characteristics of medical images, we developed the cutting-splicing data augmentation (CS-DA) method, a novel data augmentation technology for medical image segmentation. CS-DA augments the dataset by splicing different position components cut from different original medical images into a new image. The characteristics of the medical image result in the new image having the same layout as and similar appearance to the original image. Compared with classical data augmentation technologies, CS-DA is simpler and more robust. Moreover, CS-DA does not introduce any noise or fake information into the newly created image. Results: To explore the properties of CS-DA, many experiments are conducted on eight diverse datasets. On the training dataset with the small sample size, CS-DA can effectively increase the performance of the segmentation model. When CS-DA is used together with classical data augmentation technologies, the performance of the segmentation model can be further improved and is much better than that of CS-DA and classical data augmentation separately. We also explored the influence of the number of components, the position of the cutting line, and the splicing method on the CS-DA performance. Conclusions: The excellent performance of CS-DA in the experiment has confirmed the effectiveness of CS-DA, and provides a new data augmentation idea for the small sample segmentation task.
