This paper introduces panoptica, a versatile and performance-optimized package designed for computing instance-wise segmentation quality metrics from 2D and 3D segmentation maps. panoptica addresses the limitations of existing metrics and provides a modular framework that complements the original intersection over union-based panoptic quality with other metrics, such as the distance metric Average Symmetric Surface Distance. The package is open-source, implemented in Python, and accompanied by comprehensive documentation and tutorials. panoptica employs a three-step metrics computation process to cover diverse use cases. The efficacy of panoptica is demonstrated on various real-world biomedical datasets, where an instance-wise evaluation is instrumental for an accurate representation of the underlying clinical task. Overall, we envision panoptica as a valuable tool facilitating in-depth evaluation of segmentation methods.

翻译：摘要：本文介绍Panoptica，一个专为计算2D和3D分割地图的实例级分割质量指标而设计的多功能、性能优化包。Panoptica解决了现有指标的局限性，提供了一个模块化框架，可将基于交并比的原始全景质量与其他指标（如距离度量平均对称表面距离）互补结合。该包开源，采用Python实现，并配有完整的文档和教程。Panoptica采用三步指标计算流程以覆盖多样化的应用场景。通过在真实生物医学数据集上的实例级评估（这对于准确反映底层临床任务至关重要），验证了其有效性。总体而言，我们展望Panoptica将成为促进分割方法深度评估的有价值工具。