发起肾病理分割挑战赛，推动慢性肾病精准诊断
KPIs 2024 Challenge: Advancing Glomerular Segmentation from Patch- to Slide-Level
- 构建包含超1万颗肾小球的标注数据集，覆盖多种肾病模型
- 在全切片图像上实现肾小球分割与检测，最高Dice达0.89
- 适合医学图像分析、病理人工智能研究者参考
慢性肾病（CKD）是全球重大健康问题，影响超10%人口并导致高死亡率。尽管肾活检仍是诊断金标准，但缺乏全面的肾病理分割基准严重制约领域进展。为此，我们组织了肾病理图像分割挑战赛（KPIs Challenge），引入一个包含60余张周期性酸-希夫（PAS）染色全切片图像的数据集，涵盖前临床啮齿类动物模型，共标注超10,000个肾小球。挑战赛设两个任务：切片级分割与全切片图像分割检测，采用骰子相似系数（DSC）和F1分数评估。通过激励适应多种肾病模型与组织状态的创新分割方法，该挑战旨在推进肾病理分析、建立新基准，并支持疾病研究与诊断中的精确、大规模量化。
原文摘要 · Abstract (English)
Chronic kidney disease (CKD) is a major global health issue, affecting over 10% of the population and causing significant mortality. While kidney biopsy remains the gold standard for CKD diagnosis and treatment, the lack of comprehensive benchmarks for kidney pathology segmentation hinders progress in the field. To address this, we organized the Kidney Pathology Image Segmentation (KPIs) Challenge, introducing a dataset that incorporates preclinical rodent models of CKD with over 10,000 annotated glomeruli from 60+ Periodic Acid Schiff (PAS)-stained whole slide images. The challenge includes two tasks, patch-level segmentation and whole slide image segmentation and detection, evaluated using the Dice Similarity Coefficient (DSC) and F1-score. By encouraging innovative segmentation methods that adapt to diverse CKD models and tissue conditions, the KPIs Challenge aims to advance kidney pathology analysis, establish new benchmarks, and enable precise, large-scale quantification for disease research and diagnosis.
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