ArticleNature methods2025
CelloType: a unified model for segmentation and classification of tissue images.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed.
- TiLCA-Net: nuclei segmentation and classification in pathological images via task-specialized attention and efficient decoding.Quantitative imaging in medicine and surgery · 2026Article
- 3D multi-omics tumour atlases: from technology to biology and clinical translation.Nature reviews. Cancer · 2026Review
- Transforming subcellular spatial transcriptomics: deep learning models for cell segmentation.Nucleic acids research · 2026Review
- CellTune: an integrative software for accurate cell classification in spatial proteomics.Nature methods · 2026Article
- Patient-derived resources for decoding and targeting brain metastases ecosystems.EMBO molecular medicine · 2026Review
- Prototype-based AI triage for 3D pathology.bioRxiv : the preprint server for biology · 2026Article
- Celldetective, an AI-enhanced image analysis tool for unraveling dynamic cell interactions.eLife · 2026Article
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- Imaging-Based Spatial Transcriptomics: Data Interpretation Methods and Biomedical Applications.Biology · 2026Review
- Multi-task deep learning assists detection and diagnosis of gliomas and brain metastases.NPJ digital medicine · 2026Article
- Advancing single-cell omics and cell-based therapeutics with quantum computing.Nature reviews. Molecular cell biology · 2026Review
- MRR-YOLO: an instance segmentation technique for ground-based cloud images.Scientific reports · 2026Article
- Article
- Systematic evaluation of computational methods for cell segmentation.Briefings in bioinformatics · 2026Article
- Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- ImPartial: Multi-channel Whole-Cell Segmentation using Partial Annotations.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026Article
- Spatial ploidy inference using quantitative imaging.Cell reports methods · 2025Article
- Biophysical simulation enables segmentation and nervous system atlas mapping for image first spatial omics.NPJ systems biology and applications · 2025Article
- A foundation model for rock thin-section images analysis.Communications engineering · 2025Article
- Machine learning and transcriptomic analysis identify tubular injury biomarkers in patients with chronic kidney disease.International urology and nephrology · 2025Article
Corrections and comments
- Update of
Authors and funding
4 authors.
Funding
Abstract
Cell segmentation and classification are critical tasks in spatial omics data analysis. Here we introduce CelloType, an end-to-end model designed for cell segmentation and classification for image-based spatial omics data. Unlike the traditional two-stage approach of segmentation followed by classification, CelloType adopts a multitask learning strategy that integrates these tasks, simultaneously enhancing the performance of both. CelloType leverages transformer-based deep learning techniques for improved accuracy in object detection, segmentation and classification. It outperforms existing segmentation methods on a variety of multiplexed fluorescence and spatial transcriptomic images. In terms of cell type classification, CelloType surpasses a model composed of state-of-the-art methods for individual tasks and a high-performance instance segmentation model. Using multiplexed tissue images, we further demonstrate the utility of CelloType for multiscale segmentation and classification of both cellular and noncellular elements in a tissue. The enhanced accuracy and multitask learning ability of CelloType facilitate automated annotation of rapidly growing spatial omics data.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.