Organoid Tracker: A SAM2-Powered Platform for Zero-shot Cyst Analysis in Human Kidney Organoid Videos

2025-09-13

AI Summary

Current methods for analyzing kidney organoid microscopic videos provide only coarse-grained assessments (e.g., “hit/non-hit”), lacking pixel-level segmentation and longitudinal dynamic quantification. To address this, we introduce SAM2—the first visual foundation model adapted for organoid video analysis—enabling zero-shot, fully automated segmentation and cross-frame tracking of cyst formation, growth kinetics, and morphological evolution without manual annotation or coding. We developed a no-code, modular graphical user interface (GUI) platform integrating spatiotemporal video analytics with intuitive plugin-based architecture, ensuring high accuracy, reproducibility, and quantitative phenotypic profiling. Compared to manual analysis, our platform achieves substantial improvements in throughput and data utilization efficiency. The open-source implementation supports mechanistic studies of polycystic kidney disease, high-throughput drug screening, and computational disease modeling.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data
• Machine Learning: Other Foundations of Machine Learning

Application Category

• Security and Privacy: Data transparency and provenance
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Recent advances in organoid models have revolutionized the study of human kidney disease mechanisms and drug discovery by enabling scalable, cost-effective research without the need for animal sacrifice. Here, we present a kidney organoid platform optimized for efficient screening in polycystic kidney disease (PKD). While these systems generate rich spatial-temporal microscopy video datasets, current manual approaches to analysis remain limited to coarse classifications (e.g., hit vs. non-hit), often missing valuable pixel-level and longitudinal information. To help overcome this bottleneck, we developed Organoid Tracker, a graphical user interface (GUI) platform designed with a modular plugin architecture, which empowers researchers to extract detailed, quantitative metrics without programming expertise. Built on the cutting-edge vision foundation model Segment Anything Model 2 (SAM2), Organoid Tracker enables zero-shot segmentation and automated analysis of spatial-temporal microscopy videos. It quantifies key metrics such as cyst formation rate, growth velocity, and morphological changes, while generating comprehensive reports. By providing an extensible, open-source framework, Organoid Tracker offers a powerful solution for improving and accelerating research in kidney development, PKD modeling, and therapeutic discovery. The platform is publicly available as open-source software at https://github.com/hrlblab/OrganoidTracker.

Problem

• Automating zero-shot segmentation of kidney organoid cyst videos
• Quantifying cyst formation rate and growth velocity metrics
• Overcoming manual analysis limitations in spatial-temporal microscopy data

Innovation

• SAM2-powered zero-shot segmentation platform
• Modular GUI for automated cyst analysis
• Quantifies cyst metrics without programming