论文标题
用于在医学成像中介入的形状感知掩蔽
Shape-Aware Masking for Inpainting in Medical Imaging
论文作者
论文摘要
最近提出了indpainting作为无监督医学图像模型发现的成功深度学习技术。用于介入的面具通常与数据集无关，并且不适合在给定的解剖学类别上执行。在这项工作中，我们介绍了一种生成形状感知面罩的方法，旨在先验学习统计形状。我们假设，尽管面具的变化改善了介入模型的普遍性，但面具的形状应遵循感兴趣的器官的拓扑结构。因此，我们提出了一种基于现成的镶嵌模型和超像素过度分段算法的无监督的指导掩蔽方法，以生成各种依赖形状依赖性的掩码。腹部MR图像重建的实验结果表明，使用不规则形状掩模的方形或数据集，我们提出的掩蔽方法优于标准方法。
Inpainting has recently been proposed as a successful deep learning technique for unsupervised medical image model discovery. The masks used for inpainting are generally independent of the dataset and are not tailored to perform on different given classes of anatomy. In this work, we introduce a method for generating shape-aware masks for inpainting, which aims at learning the statistical shape prior. We hypothesize that although the variation of masks improves the generalizability of inpainting models, the shape of the masks should follow the topology of the organs of interest. Hence, we propose an unsupervised guided masking approach based on an off-the-shelf inpainting model and a superpixel over-segmentation algorithm to generate a wide range of shape-dependent masks. Experimental results on abdominal MR image reconstruction show the superiority of our proposed masking method over standard methods using square-shaped or dataset of irregular shape masks.
