# Cell segmentation

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bioRxiv Nov 6, 2026
Spatial transcriptomics skips cell segmentation first: SSAF-T types cells from a literature knowledge graph, then uses that map to guide segmentation
Spatial transcriptomics analysis has long depended on cell segmentation, whose boundaries and transcript assignments are error-prone; segmentation-free methods avoid that step but often need an external single-cell reference or a preset number of cell types. SSAF-T chains segmentation-free cell typing to segmentation: the agent CeLLMan labels clusters from a literature-grounded knowledge graph and marker genes, the labels are spread across the tissue map, and that map constrains nucleus-seeded watershed segmentation. On public data including Visium HD mouse small intestine, Stereo-seq mouse brain, Xenium mouse kidney and MERFISH mouse liver, cell-type distributions match tissue anatomy, segmentation is comparable to Baysor and Cellpose, and runs are faster.
Spatial transcriptomics analysis has long depended on cell segmentation, whose boundaries and transcript assignments are error-prone; segmentation-free methods avoid that step but often need an external single-cell reference or a preset number of cell types. SSAF-T chains segmentation-free cell typing to segmentation: the agent CeLLMan labels clusters from a literature-grounded knowledge graph and marker genes, the labels are spread across the tissue map, and that map constrains nucleus-seeded watershed segmentation. On public data including Visium HD mouse small intestine, Stereo-seq mouse brain, Xenium mouse kidney and MERFISH mouse liver, cell-type distributions match tissue anatomy, segmentation is comparable to Baysor and Cellpose, and runs are faster.
Pusan National University Seoul National University Sungkyunkwan University
Yuhui Jeong（First author）Jeongbin Park（Corresponding author）
SSAF-T CeLLMan Spatial transcriptomics Cell Type Annotation Cell segmentation
Nature Methods Oct 23, 2026
Cell segmentation in spatial transcriptomics gets a cross-platform test: SAM tops the raw score, SCS leads once coverage counts
Cell segmentation in spatial transcriptomics has lacked a cross-platform comparison with biologically meaningful metrics. A benchmark evaluates 11 image-based, transcriptomics-based and multimodal methods across 15 datasets from 5 technologies and about 2.3 million cells, scoring segmentation quality, transcript recovery, morphology, expression purity and spatial organization. On the average of 15 metrics, SAM scores highest (0.58), ahead of SAM2 (0.53), BIDCell (0.52) and SCS (0.48); after adjusting by the ratio of segmented to reference cells, SCS leads (0.34), with BIDCell and Baysor both at 0.33.
Cell segmentation in spatial transcriptomics has lacked a cross-platform comparison with biologically meaningful metrics. A benchmark evaluates 11 image-based, transcriptomics-based and multimodal methods across 15 datasets from 5 technologies and about 2.3 million cells, scoring segmentation quality, transcript recovery, morphology, expression purity and spatial organization. On the average of 15 metrics, SAM scores highest (0.58), ahead of SAM2 (0.53), BIDCell (0.52) and SCS (0.48); after adjusting by the ratio of segmented to reference cells, SCS leads (0.34), with BIDCell and Baysor both at 0.33.
Karolinska University Hospital King Abdullah University of Science and Technology Ilia State University Karolinska Institutet
Xabier Martinez‐de‐Morentin（First author, Corresponding author）Jesper N. Tegner（Corresponding author）David Gómez-Cabrero（Corresponding author）
Cell segmentation Spatial transcriptomics CellSPA Segment Anything Model
Lecture notes in computer science Sep 26, 2026
CALHippo builds a three-class, CA1–CA4-wide cell annotation library from 1 µm/px BigBrain sections and trains a UNet density model to infer cell density across the CA complex
Using newly released 1 µm/px BigBrain sections of the right hippocampus, this work presents CALHippo, a Cellular Annotation Library for the Hippocampus: an expert-validated, cell-level annotated dataset spanning all Cornu Ammonis (CA1–CA4) subfields with explicit three-class labels for excitatory neurons, inhibitory interneurons, and glial cells, together with a lower-resolution mesoscale cellular point-cloud map; high-resolution cell instances are obtained through a human-in-the-loop pipeline combining foundation-model-based segmentation, iterative expert correction, and model ensembling, then projected into 20 µm/px low-resolution BigBrain space to produce class-specific supervision maps used to train a UNet-based density estimation model, enabling slice-by-slice inference across the ful
Using newly released 1 µm/px BigBrain sections of the right hippocampus, this work presents CALHippo, a Cellular Annotation Library for the Hippocampus: an expert-validated, cell-level annotated dataset spanning all Cornu Ammonis (CA1–CA4) subfields with explicit three-class labels for excitatory neurons, inhibitory interneurons, and glial cells, together with a lower-resolution mesoscale cellular point-cloud map; high-resolution cell instances are obtained through a human-in-the-loop pipeline combining foundation-model-based segmentation, iterative expert correction, and model ensembling, then projected into 20 µm/px low-resolution BigBrain space to produce class-specific supervision maps used to train a UNet-based density estimation model, enabling slice-by-slice inference across the ful
University of Modena and Reggio Emilia
Giovanni Casari（First author）Federico Bolelli（Corresponding author）
CALHippo Cell segmentation Density Estimation

You’re all caught up

Spatial transcriptomics skips cell segmentation first: SSAF-T types cells from a literature knowledge graph, then uses that map to guide segmentation

Cell segmentation in spatial transcriptomics gets a cross-platform test: SAM tops the raw score, SCS leads once coverage counts

CALHippo builds a three-class, CA1–CA4-wide cell annotation library from 1 µm/px BigBrain sections and trains a UNet density model to infer cell density across the CA complex