发表机构
Biohub San Francisco（旧金山生物中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究如何从实时成像显微镜重建细胞谱系，提出高阶细胞追踪变换器（HOCT），以边为中心架构，借助3D几何先验解决现有方法问题，在相关测试中取得最优结果且易于微调，大幅降低跟踪误差。
AI 中文摘要
从实时成像显微镜重建细胞谱系需要跨时间链接细胞检测，包括通过细胞分裂。常见方法是构建候选图并关联跨帧的细胞分割（节点）。然而，现有方法忽略了候选跟踪图中的两个结构障碍：细胞分裂在节点嵌入空间中纠缠不同谱系路径；共享节点的边标签一致性近乎随机。我们提出了高阶细胞追踪变换器（HOCT），一种以边为中心的架构，其中候选细胞链接在3D几何先验下相互关注，解决了这两个问题。在细胞追踪挑战赛和细菌分裂基准测试中评估，HOCT在没有深度预训练图像编码器的情况下取得了最优结果。此外，该方法更易于微调，在人工参与设置下，用400个注释可使跟踪误差迅速降低59%，优于竞争变压器基线的LoRA微调（提高6.75%）。
英文摘要
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).
