Ruining Deng, Tianyuan Yao, Yucheng Tang, Junlin Guo, Siqi Lu, Juming Xiong, Lining Yu, Quan Huu Cap, Pengzhou Cai, Libin Lan, Ze Zhao, Adrian Galdran, Amit Kumar, Gunjan Deotale, Dev Kumar Das, Inyoung Paik, Joonho Lee, Geongyu Lee, Yujia Chen, Wangkai Li, Zhaoyang Li, Xuege Hou, Zeyuan Wu, Shengjin Wang, Maximilian Fischer, Lars Krämer, Anghong Du, Le Zhang, Maria Sánchez Sánchez, Helena Sánchez Ulloa, David Ribalta Heredia, Carlos Pérez de Arenaza García, Shuoyu Xu, Bingdou He, Xinping Cheng, Tao Wang, Noémie Moreau, Katarzyna Bozek, Shubham Innani, Ujjwal Baid, Kaura Solomon Kefas, Bennett A Landman, Yu Wang, Shilin Zhao, Mengmeng Yin, Haichun Yang, Yuankai Huo
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 50 Periodic Acid Schiff (PAS)-stained whole slide images. As a preclinical benchmark, this challenge is designed to evaluate and compare computational methods under controlled experimental conditions. 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 to support preclinical disease research and future translation to human kidney pathology.