Cell Segmentation in Images Without Structural Fluorescent Reporters

Anonymous

Keywords: cell segmentation, cell biology, image-based cellular assays, fluorescence microscopy, High Content Screening, phenotyping, nuclear segmentation

TL;DR: We present a method based on aggregation of fine-tuned Cellpose outputs to segment images without structural fluorescent markers

Abstract: Computational methods for image-based profiling are under active development, but their success depends on assays that can maximize the phenotypic information captured. Fluorescent protein (FP) tags and other methods to fluorescently label proteins of interest provide a range of tools to investigate virtually any cellular process under the microscope. However, fluorescence microscopy is limited in the number of FPs that can be simultaneously imaged in the same cell. Cell segmentation methods often rely on the presence of morphological markers such as nuclei and cytoplasm. Here, we present a generalist approach to overcome the need for morphological reporters and that instead uses reporters that contain biological information for the segmentation of the nucleus and cytoplasm. Our method leverages state-of-the-art pre-trained segmentation models to segment cytoplasm and nuclei on fluorescence microscopy images. For this, we propose to fine-tune generalist networks for cell and nucleus segmentation for each individual fluorescent channel and to aggregate the respective segmentation results together. We assess our methodology performance and robustness across several fine-tuning strategies and fusion methods across different cell lines and various reporter proteins of a specific cellular signaling pathway, and for different acquisition methods. This approach will allow maximizing the extraction of relevant biological information to characterize cellular processes.