{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/panoptica-instance-wise-evaluation-of-3d","title":"Panoptica -- instance-wise evaluation of 3D semantic and instance segmentation maps","arxiv_id":"2312.02608","date":"2023-12-05","proceeding":null,"authors":["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"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2312.02608v1","url_pdf":"https://arxiv.org/pdf/2312.02608v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"panoptica-instance-wise-evaluation-of-3d","repo_url":"https://github.com/brainlesion/panoptica","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}