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

Xinyu Shu; Andrew Zhang; Jean Yang; Jinman Kim

cs.CVSep 12, 2026

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

Authors: Xinyu Shu, Andrew Zhang, Jean Yang, Jinman Kim

Organizations: School of Computer Science, The University of Sydney, Australia · Sydney Precision Data Science Centre, The University of Sydney, Australia · School of Mathematics and Statistics, The University of Sydney, Australia · Charles Perkins Centre, The University of Sydney, Australia

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 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.

Figures & tables

Figure 1: Overview of explicit multi-method consensus construction and MARC. (a) Explicit consensus averages outputs from multiple segmentation pipelines using method-specific SST inputs, making consensus construction computationally intensive. (b) MARC predicts pixel-level consensus support from a candidate mask $F_{k}$ , DAPI morphology $D$ , and transcript information $T$ , and aggregates the prediction into cell-level scores. MARC requires no segmentation ensemble at inference. The same candidate cell is outlined in red.

Figure 2: Architecture of MARC for predicting pixel-level consensus support from a candidate mask, DAPI morphology, and transcript density.

Candidate input	Foreground-union L1 $\downarrow$	Dice $\uparrow$	IoU $\uparrow$	Sensitivity $\uparrow$	Precision $\uparrow$	Cell Spearman correlation $\rho$ $\uparrow$
Cellpose-SAM	0.0832	0.9007	0.8193	0.9020	0.8994	0.8172
BIDCell	0.1060	0.9029	0.8230	0.9298	0.8774	0.6251
ProSeg	0.1240	0.8844	0.7927	0.9073	0.8626	0.8802
Xenium 10x	0.0793	0.9127	0.8394	0.9283	0.8977	0.8396
Avg. over methods	0.0981	0.9002	0.8186	0.9168	0.8843	0.7905

Table 1: Performance of MARC predictions against explicit consensus targets. The best result for each metric is shown in bold.

Figure 3: Predicted versus target cell-level consensus-support scores for each candidate method on the held-out test region.

Configuration	Method	Dice $\uparrow$	IoU $\uparrow$	Sensitivity $\uparrow$	Precision $\uparrow$	Cell Spearman correlation $\rho$ $\uparrow$
Candidate-copy baseline	BIDCell	0.6990	0.5372	0.5474	0.9665	– ∗
	Cellpose-SAM	0.8878	0.7982	0.8794	0.8963	– ∗
	ProSeg	0.8028	0.6705	0.9608	0.6894	– ∗
	Xenium 10x	0.8863	0.7958	0.9070	0.8665	– ∗
	Avg. over methods	0.8189	0.7004	0.8237	0.8547	– ∗
MARC: candidate mask only	BIDCell	0.8364	0.7189	0.8031	0.8727	0.2493

Table 2: Input-channel ablation evaluated against explicit consensus targets.

Figure 4: Qualitative consensus-support predictions on the held-out test region. Rows show representative candidate cells from each segmentation method, while columns show the DAPI morphology, transcript-density map, candidate foreground mask, predicted pixel-level consensus, and predicted cell-level consensus map. Red outlines delineate the representative candidate cell in each row. Legends in the first column report the segmentation method, cell identifier, area in pixels, predicted cell-level consensus score, and crop coordinates. Brighter values in the output maps indicate stronger predicted cross-method consensus support.

Instance Segmentation

Instance segmentation identifies each object and assigns it a separate pixel-level mask in images or video.

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Spatial Transcriptomics Cross-Attention Graph Contrastive Learning

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Spatial transcriptomics jointly captures molecular state, tissue morphology, and spatial organization, yet spatial-domain methods are typically compared only at late training checkpoints, obscuring differences in convergence efficiency. We introduce MP-MNCA, a morphology-prior masked neighbor cross-attention framework that preserves the full 3,000 dimensional highly-variable-gene representation while selectively routing molecular information across spatial neighbors, with histological similarity incorporated as a continuous attention prior under a self-supervised masked neighbor-contrastive objective. Across 12 heterogeneous human dorsolateral prefrontal cortex (DLPFC) sections, MP-MNCA is evaluated against established spatial-domain baselines using both convergence trajectories and epoch-wise spatial-domain visualizations. Our results show that MP-MNCA (i) reaches coherent spatial-domain structure in substantially fewer training epochs, as reflected quantitatively by convergence curves and qualitatively by progressive spatial grids, and (ii) maintains strong clustering quality, achieving a mean peak refined ARI of 0.546 versus 0.549 for the strongest baseline (STAIG), while attaining the highest mean refined NMI of 0.657 versus 0.640. Moreover, MP-MNCA outperforms STAIG on 7 of 12 sections in ARI and 11 of 12 in NMI, demonstrating faster convergence without sacrificing spatial-domain quality.

Abstract

Instance Segmentation

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