论文标题
通过注意力和堆叠图像适应的通用性心脏结构进行分割
Generalisable Cardiac Structure Segmentation via Attentional and Stacked Image Adaptation
论文作者
论文摘要
在多中心和多供应商数据集中应对域变化仍然是心脏图像分割的挑战。在本文中，我们为心脏图像分割提出了一个可普遍的分割框架，其中涉及多中心，多供应商，多供应商数据集。提出了一个具有注意力丧失的生成对抗网络，将图像从现有源域转化为目标域，从而生成优质的合成心脏结构并扩大训练集。进一步使用了一系列数据增强技术来模拟现实世界的转化，以提高看不见的域的分割性能。左心室的平均骰子得分为90.3％，心肌的平均骰子得分为85.9％，在四个供应商的隐藏验证设置上的平均骰子得分为85.9％，为86.5％。我们表明，异质性心脏成像数据集中的域变化可以通过两个方面大大降低：1）通过学习基础目标域分布和2）堆叠的经典图像处理技术来大大降低良好质量的合成数据，以增加数据的数据。
Tackling domain shifts in multi-centre and multi-vendor data sets remains challenging for cardiac image segmentation. In this paper, we propose a generalisable segmentation framework for cardiac image segmentation in which multi-centre, multi-vendor, multi-disease datasets are involved. A generative adversarial networks with an attention loss was proposed to translate the images from existing source domains to a target domain, thus to generate good-quality synthetic cardiac structure and enlarge the training set. A stack of data augmentation techniques was further used to simulate real-world transformation to boost the segmentation performance for unseen domains.We achieved an average Dice score of 90.3% for the left ventricle, 85.9% for the myocardium, and 86.5% for the right ventricle on the hidden validation set across four vendors. We show that the domain shifts in heterogeneous cardiac imaging datasets can be drastically reduced by two aspects: 1) good-quality synthetic data by learning the underlying target domain distribution, and 2) stacked classical image processing techniques for data augmentation.
