Wu Lidan, Beechem Joseph M, Danaher Patrick
Bruker Spatial Biology, Seattle, WA, 98105, USA.
Sci Rep. 2025 Aug 20;15(1):30508. doi: 10.1038/s41598-025-08733-5.
Spatial transcriptomics (ST) faces persistent challenges in cell segmentation accuracy, which can bias biological interpretations in a spatial-dependent way. FastReseg introduces a novel algorithm that refines inaccuracies in existing image-based segmentations using transcriptomic data, without radically redefining cell boundaries. By combining image-based information with 3D transcriptomic precision, FastReseg enhances segmentation accuracy. Its key innovation, a transcript scoring system based on log-likelihood ratios, facilitates the quick identification and correction of spatial doublets caused by cell proximity or overlap in 2D. FastReseg reduces circularity in boundary derivation, and addresses computational challenges with a modular workflow designed for large datasets. The algorithm's modularity allows for seamless optimization and integration of advancements in segmentation technology. FastReseg provides a scalable, efficient solution to improve the quality and interpretability of ST data, ensuring compatibility with evolving segmentation methods and enabling more accurate biological insights.
空间转录组学（ST）在细胞分割准确性方面面临持续挑战，这可能以空间依赖的方式使生物学解释产生偏差。FastReseg引入了一种新颖的算法，该算法利用转录组数据改进现有基于图像的分割中的不准确之处，而无需从根本上重新定义细胞边界。通过将基于图像的信息与3D转录组精度相结合，FastReseg提高了分割准确性。其关键创新是基于对数似然比的转录本评分系统，有助于快速识别和纠正由二维中细胞接近或重叠导致的空间双峰。FastReseg减少了边界推导中的循环性，并通过为大型数据集设计的模块化工作流程解决了计算挑战。该算法的模块化允许对分割技术的进展进行无缝优化和集成。FastReseg提供了一种可扩展、高效的解决方案，以提高ST数据的质量和可解释性，确保与不断发展的分割方法兼容，并实现更准确的生物学见解。
