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

TL;DR

The KPIs 2024 Challenge introduces a new dataset and benchmarks for glomerular segmentation in kidney pathology, aiming to improve diagnosis and research in CKD through advanced image analysis methods.

Contribution

This work presents a comprehensive dataset and challenge for patch- and slide-level glomerular segmentation, fostering innovation and establishing benchmarks in kidney pathology analysis.

Findings

Over 10,000 annotated glomeruli in 60+ whole slide images.

Evaluation metrics include DSC and F1-score.

Encourages development of robust segmentation methods for diverse CKD models.

Abstract

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.