Higher-Order Cell Tracking Transformer

• URL: http://arxiv.org/abs/2607.11754v1
• Date: Mon, 13 Jul 2026 16:12:29 GMT
• Title: Higher-Order Cell Tracking Transformer
• Abstract summary: Cell divisions entangle distinct lineage paths in the node embedding space.<n> edges sharing a node have near-random label agreement.<n>We propose an edge-centric architecture in which candidate cell links attend to one another under a 3D geometric prior.
• Score: 1.3356260369011272
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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).

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