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MARC: Morphology-Aware Regression of Consensus for Cell Segmentation in Subcellular Spatial Transcriptomics — arXiv:2609.13665

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.13665
License (as archived): CC0-1.0
Archived: 2026-10-16T01:52:42.783Z
SHA-256 of the archived text: be0b7b57539768bff7c2c8346b38018ba225f146c4ac1d0d91d1d61412e0b228
Stable URL of this page: https://metraipolis.com/documento/dc_4e080a9a6e67

How to cite

MARC: Morphology-Aware Regression of Consensus for Cell Segmentation in Subcellular Spatial Transcriptomics — arXiv:2609.13665 — arXiv (en), https://arxiv.org/abs/2609.13665 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4e080a9a6e67 (Metraipolis archive dc_4e080a9a6e67, sha256 be0b7b57539768bff7c2c8346b38018ba225f146c4ac1d0d91d1d61412e0b228)

BibTeX

@misc{dc4e080a9a6e67,
  title = {MARC: Morphology-Aware Regression of Consensus for Cell Segmentation in Subcellular Spatial Transcriptomics — arXiv:2609.13665},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.13665},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4e080a9a6e67; sha256 be0b7b57539768bff7c2c8346b38018ba225f146c4ac1d0d91d1d61412e0b228},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.13665. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-15 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.13665 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T01:25:23.515Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-15?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).