24小时内用现成模型完成皮肤癌病理图像分割，效果超越基线。
Cracking the PUMA Challenge in 24 Hours with CellViT++ and nnU-Net
- 用CellViT++和nnU-Net组合，无需调参直接部署。
- 组织分割Dice达0.750，优于基线0.629。
- 适合快速落地的病理分析项目参考。
自动组织分割与细胞核检测是病理学中重要的任务，有助于生物标志物提取与发现。先进黑色素瘤全景分割挑战（PUMA）旨在提升黑色素瘤病理图像中的组织分割与细胞核检测性能。不同于多数提交方案依赖复杂模型调优，本方法强调在24小时开发周期内提供可部署解决方案，采用开箱即用框架。流程结合CellViT++用于细胞核检测，nnU-Net用于组织分割。结果表明，组织分割显著提升，Dice分数达0.750，超过基线0.629。细胞核检测在两个挑战赛道上表现与基线相当。代码已公开于https://github.com/TIO-IKIM/PUMA。
原文摘要 · Abstract (English)
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.
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