发表机构
The University of Sydney; Sydney Precision Data Science Centre; Charles Perkins Centre（悉尼大学; 悉尼精准数据科学中心; 查尔斯·珀金斯中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对亚细胞空间转录组学中细胞分割缺乏可靠真实边界的问题，提出MARC框架，通过预测多方法一致性支持图并采用留一法训练，在Xenium肾脏数据上达到Dice 0.90，无需多方法推断即可近似显式一致性。
AI 中文摘要
准确的细胞分割仍然是亚细胞空间转录组学（SST）中的一个主要瓶颈，在该领域中，利用形态学图像和空间分辨的RNA转录本将组织划分为单个细胞实例。由于分割是构建细胞水平表征的基础，边界误差可能导致转录本分配错误，并影响下游分析。然而，可靠的真实边界无法获得，因为它们必须从不完整的形态学和转录信号中推断出来。此外，手动标注大量细胞耗时费力。互补分割方法之间的一致性为识别有充分支持的区域和模糊区域提供了实用的替代方案，但显式的一致性构建需要执行多个计算密集型的流程。在本研究中，我们提出了MARC（形态学感知一致性回归），一个预测SST分割的多方法一致性支持图的框架。MARC使用留一法的一致性伪目标和前景-并集一致性损失进行训练，该损失将监督集中在候选前景和一致性前景上。我们在来自Xenium肾脏组织的4,642个保留图块上评估了MARC，与显式计算的方法间一致性图相比，实现了平均Dice分数0.90，平均交并比0.82，以及平均细胞水平Spearman相关系数0.79。我们证明了预测的一致性图能够定位支持较弱的区域，同时保持基于一致性的排名，并识别出低一致性细胞以供人工审查。这些结果表明，MARC无需多方法推断即可紧密逼近显式的方法间一致性，因此有潜力促进大规模SST研究中稳健的、一致性感知的细胞分割评估。
英文摘要
Accurate cell segmentation remains a major bottleneck in subcellular spatial transcriptomics (SST), in which morphological images and spatially resolved RNA transcripts are used to partition tissues into individual cellular instances. As segmentation serves as the foundation for constructing cell-level representations, boundary errors can lead to incorrect transcript assignments and compromise downstream analyses. However, reliable ground-truth boundaries are unavailable because they must be inferred from incomplete morphological and transcript signals. Furthermore, manual annotation of a large number of cells is time-consuming. Agreement among complementary segmentation methods provides a practical surrogate for identifying well-supported and ambiguous regions, but explicit consensus construction requires executing multiple computationally intensive pipelines. In this study, we propose MARC (Morphology-Aware Regression of Consensus), a framework that predicts a multi-method consensus-support map for SST segmentation. MARC is trained with leave-one-method-out consensus pseudo-targets and a Foreground-Union Consensus Loss that focuses supervision on candidate and consensus foreground. We evaluated MARC on 4,642 held-out tiles from Xenium kidney tissue, achieving a mean Dice score of 0.90, a mean intersection-over-union of 0.82, and a mean cell-level Spearman correlation of 0.79 against explicitly computed cross-method consensus maps. We demonstrate that the predicted consensus maps localise weakly supported regions while preserving consensus-based rankings and identifying low-consensus cells for manual review. These results show that MARC closely approximates explicit cross-method consensus without multi-method inference and therefore has the potential to facilitate robust, consensus-aware evaluation of cell segmentation in large-scale SST studies.
Comments8 pages, 4 figures
