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English(EN) MARC: Morphology-Aware Regression of Consensus for Cell Segmentation in Subcellular Spatial Transcriptomics

新的MARC框架改进了空间转录组学中的细胞分割

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了MARC，一种用于亚细胞空间转录组学中细胞分割的新型框架。该方法通过学习多种分割技术来预测共识支持图，从而绕过了计算密集型多方法推理的需要。在Xenium肾脏组织数据上，MARC实现了0.90的平均Dice分数和0.82的平均交并比，证明了其识别弱支持区域和标记低共识细胞以供审查的能力。 AI

影响 该框架通过提高细胞分割的准确性和效率，有望简化大规模空间转录组学研究中的分析。

排序理由 该集群包含一篇详细介绍新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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新的MARC框架改进了空间转录组学中的细胞分割

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该集群包含一篇详细介绍新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Xinyu Shu, Andrew Zhang, Jean Yang, Jinman Kim ·

    MARC：细胞亚细胞空间转录组学中共识的形态感知回归

    arXiv:2609.13665v1 Announce Type: new Abstract: 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 instance…

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