XXL-Computed Tomography (XXL-CT) is able to produce large scale volume datasets of scanned objects such as crash tested cars, sea and aircraft containers or cultural heritage objects. The acquired image data consists of volumes of up to and above $$\hbox {10,000}^{3}$$ voxels which can relate up to many terabytes in file size and can contain multiple 10,000 of different entities of depicted objects. In order to extract specific information about these entities from the scanned objects in such vast datasets, segmentation or delineation of these parts is necessary. Due to unknown and varying properties (shapes, densities, materials, compositions) of these objects, as well as interfering acquisition artefacts, classical (automatic) segmentation is usually not feasible. Contrarily, a complete manual delineation is error-prone and time-consuming, and can only be performed by trained and experienced personnel. Hence, an interactive and partial segmentation of so-called “chunks” into tightly coupled assemblies or sub-assemblies may help the assessment, exploration and understanding of such large scale volume data. In order to assist users with such an (possibly interactive) instance segmentation for the data exploration process, we propose to utilize delineation algorithms with an approach derived from flood filling networks. We present primary results of a flood filling network implementation adapted to non-destructive testing applications based on large scale CT from various test objects, as well as real data of an airplane and describe the adaptions to this domain. Furthermore, we address and discuss segmentation challenges due to acquisition artefacts such as scattered radiation or beam hardening resulting in reduced data quality, which can severely impair the interactive segmentation results.

中文翻译：

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探索用于 XXL 体积和散装材料 CT 数据实例分割的洪水填充网络

XXL 计算机断层扫描 (XXL-CT) 能够生成扫描对象的大规模体积数据集，例如碰撞测试的汽车、海运和飞机集装箱或文化遗产对象。所获取的图像数据由多达 $$\hbox {10,000}^{3}$$ 体素的体积组成，其文件大小可以达到数 TB，并且可以包含多个 10,000 个不同实体的描绘对象。为了从如此庞大的数据集中的扫描对象中提取有关这些实体的特定信息，需要对这些部分进行分割或描绘。由于这些物体的未知和变化的特性（形状、密度、材料、成分），以及干扰的采集人工制品，经典（自动）分割通常是不可行的。相反，完整的手动描绘容易出错且耗时，并且只能由经过培训且经验丰富的人员执行。因此，将所谓的“块”交互式和部分分割成紧密耦合的组件或子组件可能有助于评估、探索和理解此类大规模数据。为了帮助用户在数据探索过程中进行这样的（可能是交互式的）实例分割，我们建议使用划定算法以及源自洪水填充网络的方法。我们展示了基于来自各种测试对象的大规模 CT 以及飞机的真实数据的适用于无损测试应用的洪水填充网络实施的主要结果，并描述了对该领域的适应。此外，