{"title":"cell- app:一种可推广的细胞标注和细胞分割模型训练方法。","authors":"Anish J Virdi, Ajit P Joglekar","doi":"10.1091/mbc.E25-02-0076","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":18735,"journal":{"name":"Molecular Biology of the Cell","volume":" ","pages":"mbcE25020076"},"PeriodicalIF":2.7000,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cell-APP: A generalizable method for cell annotation and cell-segmentation model training.\",\"authors\":\"Anish J Virdi, Ajit P Joglekar\",\"doi\":\"10.1091/mbc.E25-02-0076\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":18735,\"journal\":{\"name\":\"Molecular Biology of the Cell\",\"volume\":\" \",\"pages\":\"mbcE25020076\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2025-09-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Molecular Biology of the Cell\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1091/mbc.E25-02-0076\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CELL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Molecular Biology of the Cell","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1091/mbc.E25-02-0076","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
Cell-APP: A generalizable method for cell annotation and cell-segmentation model training.
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.
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.