Bibliographic Details
| Title: |
KPIs 2024 Challenge: Advancing Glomerular Segmentation from Patch- to Slide-Level |
| Authors: |
Deng, Ruining, Yao, Tianyuan, Tang, Yucheng, Guo, Junlin, Lu, Siqi, Xiong, Juming, Yu, Lining, Cap, Quan Huu, Cai, Pengzhou, Lan, Libin, Zhao, Ze, Galdran, Adrian, Kumar, Amit, Deotale, Gunjan, Das, Dev Kumar, Paik, Inyoung, Lee, Joonho, Lee, Geongyu, Chen, Yujia, Li, Wangkai, Li, Zhaoyang, Hou, Xuege, Wu, Zeyuan, Wang, Shengjin, Fischer, Maximilian, Kramer, Lars, Du, Anghong, Zhang, Le, Sanchez, Maria Sanchez, Ulloa, Helena Sanchez, Heredia, David Ribalta, Garcia, Carlos Perez de Arenaza, Xu, Shuoyu, He, Bingdou, Cheng, Xinping, Wang, Tao, Moreau, Noemie, Bozek, Katarzyna, Innani, Shubham, Baid, Ujjwal, Kefas, Kaura Solomon, Landman, Bennett A., Wang, Yu, Zhao, Shilin, Yin, Mengmeng, Yang, Haichun, Huo, Yuankai |
| Publication Year: |
2025 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence |
| More Details: |
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. |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2502.07288 |
| Accession Number: |
edsarx.2502.07288 |
| Database: |
arXiv |
