基于公开数据集的结直肠癌分级分割挑战赛，推动病理图像自动化分析
Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge
- 使用103张全切片图像和像素级标注，构建标准化评估基准
- 六支队伍超越Swin Transformer基线（F-score 62.92）
- 适合医学图像分析与病理自动化研究者参考
结直肠癌（CRC）是全球第三大常见癌症，也是第二大癌症致死原因。准确的组织病理学分级对预后判断和治疗方案制定至关重要，但目前仍依赖主观判断，易受观察者差异影响，且全球训练有素的病理科医生严重短缺。为推动自动化、标准化解决方案的发展，我们组织了基于公开METU CCTGS数据集的ICIP结直肠癌肿瘤分级与分割大挑战。该数据集包含103张全切片图像，涵盖五类组织的专家级像素级标注。参赛团队通过Codalab提交分割掩码，采用宏F-score和mIoU等指标评估性能。共有39支队伍参与，其中六支超越Swin Transformer基线（F-score 62.92）。本文综述了挑战赛背景、数据集构成及表现最优方法。
原文摘要 · Abstract (English)
Colorectal cancer (CRC) is the third most diagnosed cancer and the second leading cause of cancer-related death worldwide. Accurate histopathological grading of CRC is essential for prognosis and treatment planning but remains a subjective process prone to observer variability and limited by global shortages of trained pathologists. To promote automated and standardized solutions, we organized the ICIP Grand Challenge on Colorectal Cancer Tumor Grading and Segmentation using the publicly available METU CCTGS dataset. The dataset comprises 103 whole-slide images with expert pixel-level annotations for five tissue classes. Participants submitted segmentation masks via Codalab, evaluated using metrics such as macro F-score and mIoU. Among 39 participating teams, six outperformed the Swin Transformer baseline (62.92 F-score). This paper presents an overview of the challenge, dataset, and the top-performing methods
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