论文标题
用于分割大型3D体积的几何积极学习
Geometric Active Learning for Segmentation of Large 3D Volumes
论文作者
论文摘要
分割，即将体积数据分配到组件中，在许多图像处理应用程序中都是至关重要的任务，因为可以生成此类数据。如今，大多数现有的应用程序，尤其是CNNS，都利用了VoxelWise分类系统，这些系统需要在大量注释的培训量上进行培训。但是，在许多实际应用中，这些数据集很少可用，并且注释的产生是耗时且繁琐的。在本文中，我们引入了一种基于几何特征的主动学习的新型紫og虫分割方法。我们的方法使用互动提供的种子点来完全基于本地信息来训练自动voxelwise分类器。域知识和局部处理的临时融合的组合导致了一种灵活而有效的分割方法，该方法适用于三维体积，无需大小限制。我们通过将其应用于选定的计算机断层扫描扫描来说明我们的方法的潜力和灵活性，在该扫描中，我们执行不同的分割任务来扫描来自不同域和不同尺寸的扫描。
Segmentation, i.e., the partitioning of volumetric data into components, is a crucial task in many image processing applications ever since such data could be generated. Most existing applications nowadays, specifically CNNs, make use of voxelwise classification systems which need to be trained on a large number of annotated training volumes. However, in many practical applications such data sets are seldom available and the generation of annotations is time-consuming and cumbersome. In this paper, we introduce a novel voxelwise segmentation method based on active learning on geometric features. Our method uses interactively provided seed points to train a voxelwise classifier based entirely on local information. The combination of an ad hoc incorporation of domain knowledge and local processing results in a flexible yet efficient segmentation method that is applicable to three-dimensional volumes without size restrictions. We illustrate the potential and flexibility of our approach by applying it to selected computed tomography scans where we perform different segmentation tasks to scans from different domains and of different sizes.
