Panoptica -- instance-wise evaluation of 3D semantic and instance
  segmentation maps
Florian Kofler Hendrik Möller Josef A. Buchner Ezequiel de la Rosa Ivan Ezhov Marcel Rosier Isra Mekki Suprosanna Shit Moritz Negwer Rami Al-Maskari Ali Ertürk Shankeeth Vinayahalingam Fabian Isensee Sarthak Pati Daniel Rueckert Jan S. Kirschke Stefan K. Ehrlich Annika Reinke Bjoern Menze Benedikt Wiestler Marie Piraud
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.