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.

该论文介绍了一个名为 panoptica 的软件包，用于计算 2D 和 3D 分割映射的实例级分割质量指标。panoptica 通过引入不同的指标，如平均对称面距离，弥补了现有指标的局限性，并提供了一个模块化框架。该软件包是开源的，使用 Python 实现，并附带详细的文档和教程。panoptica 使用三步计算流程，适用于不同的使用案例。该论文在各种真实的生物医学数据集上展示了 panoptica 的有效性，其中实例级评估对于准确表示潜在的临床任务至关重要。总的来说，我们认为 panoptica 是一个有价值的工具，可以促进分割方法的深入评估。