论文标题
植入合成病变，用于改善CT检查中的肝病变量分割
Implanting Synthetic Lesions for Improving Liver Lesion Segmentation in CT Exams
论文作者
论文摘要
使用计算机断层扫描（CT）检查的监督病变细分算法的成功取决于可用于培训的样品的数量和可变性。注释此类数据构成挑战本身，但数据集中病变的变异性也取决于不同类型病变的流行。这种现象为病变分割算法增加了固有的偏见，在不同的可能性中，使用积极的数据增强方法可以减少。在本文中，我们提出了一种将现实病变植入CT切片中的方法，以提供一组丰富且可控的训练样本集，并最终改善语义分割网络性能，以划分CT检查中的划定病变。我们的结果表明，植入合成病变不仅可以改善（大约12 \％）考虑不同架构的分割性能，而且在不同的图像综合网络中这种改进是一致的。我们得出的结论是，在大小，密度，形状和位置方面，合成病变的变异性似乎可以提高CT切片中肝病变分割的分割模型的性能。
The success of supervised lesion segmentation algorithms using Computed Tomography (CT) exams depends significantly on the quantity and variability of samples available for training. While annotating such data constitutes a challenge itself, the variability of lesions in the dataset also depends on the prevalence of different types of lesions. This phenomenon adds an inherent bias to lesion segmentation algorithms that can be diminished, among different possibilities, using aggressive data augmentation methods. In this paper, we present a method for implanting realistic lesions in CT slices to provide a rich and controllable set of training samples and ultimately improving semantic segmentation network performances for delineating lesions in CT exams. Our results show that implanting synthetic lesions not only improves (up to around 12\%) the segmentation performance considering different architectures but also that this improvement is consistent among different image synthesis networks. We conclude that increasing the variability of lesions synthetically in terms of size, density, shape, and position seems to improve the performance of segmentation models for liver lesion segmentation in CT slices.
