论文标题
基于框架插值，用于提高医疗图像分割精度的固定图像增强
Inter-slice image augmentation based on frame interpolation for boosting medical image segmentation accuracy
论文作者
论文摘要
我们介绍了切片间图像增强的想法，从而在两个连续图像之间增加了医学图像的数量和相应的分割标签，以提高医疗图像分割精度。与传统的医学成像中的传统数据增强方法不同，仅通过使用简单的参数化转换（例如旋转，翻转，缩放等）添加新的虚拟样本来直接增加训练样本的数量，我们旨在基于两个连续图像之间的关系增强数据，这不仅增加了训练样本的数量，还增加了训练样本的信息。为此，我们提出了一种基于框架交互的数据增强方法，以生成中间的医学图像和两个连续图像之间的相应分割标签。我们分别在SLIVER07和CHAOS2019上训练并测试了有监督的U-NET肝脏分割网络，分别通过增强训练样本进行训练，并获得与常规增强方法相比，获得显着改善的分割分数。
We introduce the idea of inter-slice image augmentation whereby the numbers of the medical images and the corresponding segmentation labels are increased between two consecutive images in order to boost medical image segmentation accuracy. Unlike conventional data augmentation methods in medical imaging, which only increase the number of training samples directly by adding new virtual samples using simple parameterized transformations such as rotation, flipping, scaling, etc., we aim to augment data based on the relationship between two consecutive images, which increases not only the number but also the information of training samples. For this purpose, we propose a frame-interpolation-based data augmentation method to generate intermediate medical images and the corresponding segmentation labels between two consecutive images. We train and test a supervised U-Net liver segmentation network on SLIVER07 and CHAOS2019, respectively, with the augmented training samples, and obtain segmentation scores exhibiting significant improvement compared to the conventional augmentation methods.
