Tiger Lao

Ultrack improves cell tracking across 2D, 3D, and multi-channel time-lapse recordings by integrating multiple segmentations and temporal consistency, achieving state-of-the-art performance on benchmark datasets.

The paper develops robust virtual staining methods for cellular landmarks using deep learning, showing improved performance across cell types and imaging conditions.

The paper introduces a lipid-based transfection method for zebrafish embryos as an alternative to microinjection, improving efficiency and throughput for genetic studies.

Zebrafish embryos sequenced individually across ten developmental stages, paired with light-sheet lineage reconstruction, form an atlas and browser for in silico fate mapping.