FiloAnalyzer: a deep learning approach for cell filopodia segmentation

Purpose: Filopodia are thin, finger-like extensions that project from the surface of cells. Present in various cell types, analyzing these structures can yield significant insights into cellular behavior and function. However, a manual analysis is impractical, resulting in a bottleneck in processing large-scale datasets. Methods: This paper introduces FiloAnalyzer, an open-source toolbox for automatically analyzing cell filopodia. The toolbox is powered with a user-friendly GUI, allowing the segmentation of batches of microscopy images and extracting a set of quantitative parameters, incorporating both a deep learning-based and an image-processing method. As data and annotation scarcity are typical of this domain, we compare popular ImageNet supervised to self-supervised pre-training, adopting state-of-the-art contrastive, self-distillation, and generative approaches based on diffusion models in an attempt to maximize segmentation performance in a low-data regime. Results: We validate our toolbox on a dataset of annotated fluorescent images of neural crest cells, benchmarking our results with three popular filopodia segmentation toolboxes. Furthermore, we show how self-supervision can be a promising tool to deal with the limited availability of annotated data in this domain, obtaining a significant improvement over ImageNet supervised pre-training, with as few as ten annotated images. Conclusion: Our experiments show that the deep learning pipeline outperforms image processing-based available alternatives. We release both an annotated dataset and the toolbox as open-source to foster further research on this topic. The toolbox code and dataset are available at https://github.com/Malga-Vision/FiloAnalyzerToolbox.