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This paper introduces panoptica, a versatile and performance-optimizedpackage designed for computing instance-wise segmentation quality metrics from2D and 3D segmentation maps. panoptica addresses the limitations of existingmetrics and provides a modular framework that complements the originalintersection over union-based panoptic quality with other metrics, such as thedistance metric Average Symmetric Surface Distance. The package is open-source,implemented in Python, and accompanied by comprehensive documentation andtutorials. panoptica employs a three-step metrics computation process to coverdiverse use cases. The efficacy of panoptica is demonstrated on variousreal-world biomedical datasets, where an instance-wise evaluation isinstrumental for an accurate representation of the underlying clinical task.Overall, we envision panoptica as a valuable tool facilitating in-depthevaluation of segmentation methods.

Panoptica -- instance-wise evaluation of 3D semantic and instance segmentation maps