Cracking the PUMA Challenge in 24 Hours with CellViT++ and nnU-Net

Automatic tissue segmentation and nuclei detection is an important task in pathology, aiding in biomarker extraction and discovery. The panoptic segmentation of nuclei and tissue in advanced melanoma (PUMA) challenge aims to improve tissue segmentation and nuclei detection in melanoma histopathology. Unlike many challenge submissions focusing on extensive model tuning, our approach emphasizes delivering a deployable solution within a 24-hour development timeframe, using out-of-the-box frameworks. The pipeline combines two models, namely CellViT++ for nuclei detection and nnU-Net for tissue segmentation. Our results demonstrate a significant improvement in tissue segmentation, achieving a Dice score of 0.750, surpassing the baseline score of 0.629. For nuclei detection, we obtained results comparable to the baseline in both challenge tracks. The code is publicly available atthis https URL.

@article{shahamiri2025_2503.12269,
  title={ Cracking the PUMA Challenge in 24 Hours with CellViT++ and nnU-Net },
  author={ Negar Shahamiri and Moritz Rempe and Lukas Heine and Jens Kleesiek and Fabian Hörst },
  journal={arXiv preprint arXiv:2503.12269},
  year={ 2025 }
}