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.
翻译:自生成这些数据以来,将体积数据分成各个组成部分,是许多图像处理应用程序中的一项关键任务。如今,大多数现有应用程序,特别是CNN,都使用需要大量附加说明的培训培训培训培训的反氧化素分类系统。然而,在许多实际应用中,这类数据集很少可用,生成说明既费时又繁琐。在本文件中,我们采用了一种基于积极学习几何特征的新颖的恶性分解方法。我们的方法使用交互提供的种子点来训练完全基于本地信息的反氧化分类器。将域知识和本地处理结合成一种灵活而高效的分解方法,在不受尺寸限制的情况下适用于三维体体体体积。我们用这种方法来说明我们的方法的潜力和灵活性,将它应用到我们执行不同领域和不同尺寸扫描的不同分解任务的地方进行选定的计算成色扫描。