Important Updates: 

October 10th, 2024: The test data has been released!

September 19th, 2024: The challenge leaderboard is updated!  We now have two leaderboards for Task2:

1. Segmentation Leaderboard – Ranking based on both Dice and F1 scores.

2. Detection Leaderboard – Ranking based on F1 score only.

September 15th, 2024: Paper submission policy has been updated. The eligible team in the official ranking will receive further instructions in the next few days.

September 15th, 2024: The challenge leaderboard is released! 🎉🎉🎉

August 2nd, 2024: The submission phase ended. 

July 18th, 2024: The final testing submission portal is open. Please submit your Docker before 08/01/2024, 11:59 PM PST. For details please find KPIs 2024https://sites.google.com/view/kpis2024/evaluation 

July 11th, 2024: Please cite the following paper if you use the KPIs challenge data in your publication.  [link of the paper]

Yucheng Tang, Yufan He, Vishwesh Nath, Pengfei Guo, Ruining Deng, Tianyuan Yao, Quan Liu, Can Cui, Mengmeng Yin, Ziyue Xu, Holger Roth, Daguang Xu, Haichun Yang, Yuankai Huo  "HoloHisto: End-to-end Gigapixel WSI Segmentation with 4K Resolution Sequential Tokenization." arXiv preprint arXiv:2407.03307 (2024).

Jun 4th, 2024:  We have identified an issue with the previously released data, which could affect the training and evaluation process.  Please see the details in the Data section and download the updated Data v1.1 to ensure accurate model development and evaluation. 

Chronic kidney disease (CKD) poses a significant health risk, causing more deaths annually than breast and prostate cancer combined. Impacting over 10% of the worldwide population, it affects upwards of 800 million individuals. Kidney biopsy, encompassing both open and percutaneous methods, is the gold standard for diagnosing and guiding the treatment of CKD. 

In pathological image analysis, particularly in kidney disease, tissue segmentation is of paramount importance. The rise of deep learning has been transformative in kidney pathology image segmentation, yet it has also exposed a lack of comprehensive benchmarks for developing and evaluating these techniques. A major hurdle has been the scarcity of large-scale disease data in existing public datasets for kidney pathology segmentation, as they predominantly comprise samples from normal patients. This is mainly because the tissue samples from humans are typically obtained through needle biopsies, yielding only small tissue samples. Consequently, there's a pressing need to release extensive kidney pathology digital data spanning various CKD disease models. 

In our challenge, we've expanded the dataset from CKD disease models by utilizing preclinical animal models, particularly whole kidney sections from diseased rodents. The primary rationale for using rodent data is the morphological similarity between rodent and human kidney pathologies, making them a prevalent choice in pre-clinical medical research and drug discovery. Secondly, whole kidney sections can be sourced from comprehensive disease models, providing an abundance of tissue in each whole slide image (WSI). This is a significant advantage, as a single WSI from these models can encompass more tissue content than what would be achievable from thousands of needle biopsies in human disease models, an approach that is often impractical. 

The Kidney Pathology Image Segmentation (KPIs) challenge encompasses a broad spectrum of kidney disease models, including normal and multiple specific CKD conditions, derived from preclinical rodent models. As a pioneering effort in the MICCAI community, the challenge features an extensive collection of 10,000 normal and diseased glomeruli from over 60 Periodic acid Schiff (PAS) stained whole slide images. Each image includes nephrons, with each nephron containing a glomerulus, and a small cluster of blood vessels. The objective for participants is to develop algorithms that can precisely segment glomeruli at a pixel level. To the best of our knowledge, this represents the first MICCAI challenge focused exclusively on segmenting functional units in kidney pathology across various CKD disease models.