Cell-APP: A generalizable method for cell annotation and cell-segmentation model training.

IF 2.7 3区 生物学 Q3 CELL BIOLOGY
Anish J Virdi, Ajit P Joglekar
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引用次数: 0

Abstract

Deep learning-based segmentation models can accelerate the analysis of high-throughput microscopy data by automatically identifying and classifying cells in images. However, the datasets needed to train these models are typically assembled via laborious hand-annotation. This limits their scale and diversity, which in turn limits model performance. We present Cell-APP (Cellular Annotation and Perception Pipeline), a tool that automates the annotation of high-quality training data for transmitted-light (TL) cell segmentation. Cell-APP uses two inputs-paired TL and nuclear fluorescence images-and operates in two main steps. First, it extracts each cell's location from the nuclear fluorescence channel and provides these locations to promptable deep learning models to generate cell masks. Then, it classifies each cell as mitotic or non-mitotic based on nuclear features. Together, these masks and classifications form the basis for cell segmentation training data. By training vision-transformer-based models on Cell-APP-generated datasets, we demonstrate how Cell-APP enables the creation of both cell line-specific and multi-cell line segmentation models. Cell-APP thus empowers laboratories to tailor cell segmentation models to their needs and outlines a scalable path to creating general models for the research community.

cell- app:一种可推广的细胞标注和细胞分割模型训练方法。
基于深度学习的分割模型可以通过自动识别和分类图像中的细胞来加速高通量显微镜数据的分析。然而,训练这些模型所需的数据集通常是通过费力的手工注释来组装的。这限制了它们的规模和多样性,反过来又限制了模型的性能。我们提出cell - app(细胞注释和感知管道),这是一个自动注释高质量训练数据的工具,用于透射光(TL)细胞分割。Cell-APP使用两个输入——配对的TL和核荧光图像——并通过两个主要步骤进行操作。首先,它从核荧光通道中提取每个细胞的位置,并将这些位置提供给提示深度学习模型以生成细胞掩模。然后,它根据细胞核特征将每个细胞分类为有丝分裂或非有丝分裂。总之,这些掩码和分类构成了细胞分割训练数据的基础。通过在cell - app生成的数据集上训练基于视觉转换器的模型,我们演示了cell - app如何创建细胞系特定和多细胞系分割模型。因此,cell - app使实验室能够根据自己的需要定制细胞分割模型,并概述了为研究界创建通用模型的可扩展路径。
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来源期刊
Molecular Biology of the Cell
Molecular Biology of the Cell 生物-细胞生物学
CiteScore
6.00
自引率
6.10%
发文量
402
审稿时长
2 months
期刊介绍: MBoC publishes research articles that present conceptual advances of broad interest and significance within all areas of cell, molecular, and developmental biology. We welcome manuscripts that describe advances with applications across topics including but not limited to: cell growth and division; nuclear and cytoskeletal processes; membrane trafficking and autophagy; organelle biology; quantitative cell biology; physical cell biology and mechanobiology; cell signaling; stem cell biology and development; cancer biology; cellular immunology and microbial pathogenesis; cellular neurobiology; prokaryotic cell biology; and cell biology of disease.
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