Panoptica -- instance-wise evaluation of 3D semantic and instance segmentation maps
It provides a tool for researchers in biomedical segmentation to evaluate methods more accurately, though it is incremental as it builds on existing metrics.
The paper introduces panoptica, a package for computing instance-wise segmentation quality metrics in 2D and 3D, addressing limitations of existing metrics and demonstrating efficacy on biomedical datasets.
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.