Instance segmentation of compound objects in XXL-CT imagery poses a unique challenge in non-destructive testing. This complexity arises from the lack of known reference segmentation labels, limited applicable segmentation tools, as well as partially degraded image quality. To asses recent advancements in the field of machine learning-based image segmentation, the "Instance Segmentation XXL-CT Challenge of a Historic Airplane" was conducted. The challenge aimed to explore automatic or interactive instance segmentation methods for an efficient delineation of the different aircraft components, such as screws, rivets, metal sheets or pressure tubes. We report the organization and outcome of this challenge and describe the capabilities and limitations of the submitted segmentation methods.

翻译：对XXL-CT图像中的复合对象进行实例分割，是非破坏性检测领域的一项独特挑战。其复杂性源于缺乏已知参考分割标签、适用的分割工具有限，以及部分图像质量退化。为评估机器学习图像分割领域的最新进展，举办了"飞机历史文物的实例分割XXL-CT挑战赛"。该挑战旨在探索自动或交互式实例分割方法，以高效勾勒飞机不同部件（如螺钉、铆钉、金属板材或压力管）的轮廓。本文报告了该挑战赛的组织与成果，并阐述了所提交分割方法的性能与局限性。