Cracking the PUMA Challenge in 24 Hours with CellViT++ and nnU-Net
Journal:
arXiv
Published Date:
Mar 15, 2025
Abstract
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 at
https://github.com/TIO-IKIM/PUMA.
