Pang Minxing, Roy Tarun Kanti, Wu Xiaodong, Tan Kai
Applied Mathematics and Computational Science Graduate Group, University of Pennsylvania, Philadelphia, PA, USA.
Department of Computer Science, University of Iowa, Iowa City, IA, USA.
Nat Methods. 2025 Feb;22(2):348-357. doi: 10.1038/s41592-024-02513-1. Epub 2024 Nov 22.
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.
细胞分割与分类是空间组学数据分析中的关键任务。在此，我们介绍CelloType，这是一种为基于图像的空间组学数据的细胞分割与分类而设计的端到端模型。与传统的先分割后分类的两阶段方法不同，CelloType采用多任务学习策略，将这些任务整合在一起，同时提高两者的性能。CelloType利用基于Transformer的深度学习技术来提高目标检测、分割和分类的准确性。在各种多重荧光和空间转录组图像上，它的表现优于现有的分割方法。在细胞类型分类方面，CelloType超越了由用于单个任务的最先进方法组成的模型和一个高性能的实例分割模型。使用多重组织图像，我们进一步证明了CelloType在组织中细胞和非细胞成分的多尺度分割与分类方面的效用。CelloType提高的准确性和多任务学习能力有助于对快速增长的空间组学数据进行自动注释。
