Detect and track zebrafish cells through 3D space and time.
Your goal is to develop algorithms to detect, track and link cells across time in 3D microscopy data, including accurate identification of cell divisions and lineage reconstruction. You will work with real microscopy datasets to build robust methods that can handle dense cell populations, noise and complex biological structures.
Your work will eliminate a massive manual bottleneck in biological research and help scientists quantify the building blocks of life.
Tracking cells across time in 3D microscopy is a fundamental challenge in biological research. Scientists rely on time-lapse 3D imaging to study how cells grow, interact, and evolve, but analyzing this data remains a massive bottleneck. Currently, researchers spend countless hours manually tracking cells—especially in complex datasets where thousands of visually similar cells move, deform, and divide.
While automated tools exist, they often fail under real-world conditions. High cell density, imaging noise, and irregular cell shapes cause critical errors in lineage reconstruction, limiting the scalability of these studies.
This competition provides a shared benchmark to solve this problem. Your task is to detect cells, associate them across frames, and identify division events to reconstruct accurate cell lineages. By developing robust, generalizable algorithms for 3D+time cell tracking, you will help eliminate manual effort, improve scientific reproducibility, and accelerate new discoveries in developmental biology, immunology, and disease research.