mlsanityData to 7 Oct 2026

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

arXiv 2312.02608 ↗First posted 5 Dec 2023cs.CV cs.AI cs.LGcited by 11

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 22 of its 39 references that are in the map. First posted on arXiv 5 Dec 2023.

Builds on

22 of 39 references.

References · 22

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2018Panoptic Segmentationcited by 796
  3. 2019The Liver Tumor Segmentation Benchmark (LiTS)cited by 504
  4. 2022MONAI: An open-source framework for deep learning in healthcarecited by 438
  5. 2020VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Imagescited by 111
  6. 2022ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation datasetcited by 104
  7. 2020nnU-Net for Brain Tumor Segmentationcited by 103
  8. 2021Common Limitations of Image Processing Metrics: A Picture Storycited by 58
  9. 2023The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRIcited by 35
  10. 2021Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficientcited by 26
  11. 2022blob loss: instance imbalance aware loss functions for semantic segmentationcited by 18
  12. 2020Modality-Pairing Learning for Brain Tumor Segmentationcited by 17
10 more
  1. 2020pymia: A Python package for data handling and evaluation in deep learning-based medical image analysiscited by 16
  2. 2022Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021cited by 14
  3. 2022Factorizer: A Scalable Interpretable Approach to Context Modeling for Medical Image Segmentationcited by 12
  4. 2020H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Taskcited by 10
  5. 2020Automatic Brain Tumor Segmentation with Scale Attention Networkcited by 8
  6. 2022Approaching Peak Ground Truthcited by 8
  7. 2023SortedAP: Rethinking evaluation metrics for instance segmentationcited by 8
  8. 2020Uncertainty-driven refinement of tumor-core segmentation using 3D-to-2D networks with label uncertaintycited by 6
  9. 2022Automated ischemic stroke lesion segmentation from 3D MRIcited by 2
  10. 2023Self-pruning Graph Neural Network for Predicting Inflammatory Disease Activity in Multiple Sclerosis from Brain MR Imagescited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 2; 2026: 5.

11 papers.

  1. 2024VIBESegmentator: Full Body MRI Segmentation for the NAKO and UK Biobankcited by 11
  2. 2024SPINEPS -- Automatic Whole Spine Segmentation of T2-weighted MR images using a Two-Phase Approach to Multi-class Semantic and Instance Segmentationcited by 9
  3. 2024Transformers-based architectures for stroke segmentation: A reviewcited by 5
  4. 2024ISLES'24: Final Infarct Prediction with Multimodal Imaging and Clinical Data. Where Do We Stand?cited by 4
  5. 2026Agentic Large Language Models for Training-Free Neuro-Radiological Image Analysiscited by 4
  6. 2025Automated Thoracolumbar Stump Rib Detection and Analysis in a Large CT Cohortcited by 1
  7. 2025Enhancing efficiency in paediatric brain tumour segmentation using a pathologically diverse single-center clinical datasetcited by 0
  8. 2026One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentationcited by 0
  9. 2026Redefining Instance Matching: A Unified Framework for Part-Aware Matching in Panoptic Segmentation Evaluationcited by 0
  10. 2026Adaptive Post-Processing Drives Instance-Level Detection in Stroke Lesion Segmentationcited by 0
  11. 2026Three-Phase Scribble-Adaptive Curriculum Learning for autoPETV Grand Challengecited by 0