  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.
