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KPIs 2024 Challenge: Advancing Glomerular Segmentation from Patch- to Slide-Level

Fuente: arXiv

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Main 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
Format:	Preprint
Published:	2025
Subjects:	Computer Vision and Pattern Recognition Artificial Intelligence
Online Access:	Acceder al recurso
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_version_	1866915145864708096
author	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
author_facet	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
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2502_07288
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	KPIs 2024 Challenge: Advancing Glomerular Segmentation from Patch- to Slide-Level 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 Computer Vision and Pattern Recognition Artificial Intelligence 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.
title	KPIs 2024 Challenge: Advancing Glomerular Segmentation from Patch- to Slide-Level
topic	Computer Vision and Pattern Recognition Artificial Intelligence
url	https://arxiv.org/abs/2502.07288