Exploring Flood Filling Networks for Instance Segmentation of XXL-Volumetric and Bulk Material CT Data
Exploring Flood Filling Networks for Instance Segmentation of XXL-Volumetric and Bulk Material CT Data
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DOI：
10.1007/s10921-020-00734-w
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发表时间：
2021-03-01
影响因子：
2.8
通讯作者：
Wittenberg, Thomas
中科院分区：
文献类型：
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作者：
Gruber, Roland;Gerth, Stefan;Wittenberg, Thomas
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，例如经过碰撞测试的汽车、海运和飞机集装箱或文化遗产对象。采集的图像数据由多达 10,000(3) 个体素组成，文件大小可高达数 TB，并且可包含所描绘对象的多个 10,000 个不同实体。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。由于这些对象的未知且变化的属性（形状、密度、材料、成分）以及干扰性采集伪影，经典（自动）分割通常不可行。相反，完全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来执行。因此，将所谓的“块”交互式地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体数据。为了帮助用户在数据探索过程中进行此类（可能是交互式的）实例分割，我们建议使用源自洪水填充网络的方法的描绘算法。我们展示了基于来自各种测试对象的大规模 CT 的无损检测应用的洪水填充网络实施的主要结果，以及飞机的真实数据，并描述了对该领域的适应。此外，我们还解决并讨论了由于采集伪影（例如散射辐射或光束硬化）导致的数据质量下降而导致的分割挑战，这可能会严重损害交互式分割结果。
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 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.