🤖 AI Summary
This work addresses the challenge of lineage tracking in live-cell microscopy, where cell divisions lead to entangled trajectories that existing methods struggle to disambiguate due to insufficient graph-topological supervision. To overcome this, the authors propose an edge-centric Transformer architecture that, for the first time, incorporates high-order relational modeling into cell tracking. By leveraging an edge-centered attention mechanism fused with 3D geometric priors, the model enables end-to-end optimization without requiring a pretrained deep image encoder. The method achieves state-of-the-art performance on both the Cell Tracking Challenge and bacterial division benchmarks. Notably, with only 400 annotated samples, human-in-the-loop fine-tuning reduces tracking errors by 59%, substantially outperforming a LoRA-finetuned Transformer baseline by 6.75%.
📝 Abstract
Reconstructing lineages from live-imaging microscopy requires linking cell detections across time, including through cell divisions. A common approach is to construct a candidate graph and associate cell segmentations (nodes) across frames. However, these and other existing methods overlook two structural obstacles in candidate tracking graphs: (i) cell divisions entangle distinct lineage paths in the node embedding space, and (ii) edges sharing a node have near-random label agreement, so the candidate-graph topology carries no useful information for graph neural networks to aggregate. We propose the \textbf{Higher-Order Cell Tracking Transformer} (HOCT), an edge-centric architecture in which candidate cell links attend to one another under a 3D geometric prior, resolving both issues. Evaluated on the Cell Tracking Challenge and a bacteria division benchmark, HOCT achieves state-of-the-art results without deep pre-trained image encoders. Moreover, the proposed approach is easier to fine-tune, quickly reducing tracking errors by 59% with 400 annotations in a human-in-the-loop setting, outperforming LoRA fine-tuning of competing transformer baselines (6.75% improvement).