Journal article
CelloType: a unified model for segmentation and classification of tissue images
Nature methods, Vol.22(2), pp.348-357
02/2025
DOI: 10.1038/s41592-024-02513-1
PMCID: PMC11810770
PMID: 39578628
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.
Details
- Title: Subtitle
- CelloType: a unified model for segmentation and classification of tissue images
- Creators
- Minxing Pang - University of PennsylvaniaTarun Kanti Roy - University of IowaXiaodong Wu - University of IowaKai Tan - University of Pennsylvania
- Resource Type
- Journal article
- Publication Details
- Nature methods, Vol.22(2), pp.348-357
- DOI
- 10.1038/s41592-024-02513-1
- PMID
- 39578628
- PMCID
- PMC11810770
- NLM abbreviation
- Nat Methods
- ISSN
- 1548-7091
- eISSN
- 1548-7105
- Publisher
- NATURE PORTFOLIO
- Grant note
- U2C CA233285 / NCI NIH HHS U54 HL165442 / NHLBI NIH HHS
- Language
- English
- Electronic publication date
- 11/22/2024
- Date published
- 02/2025
- Academic Unit
- Electrical and Computer Engineering; Radiation Oncology; The Iowa Institute for Biomedical Imaging
- Record Identifier
- 9984751758602771
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