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.

The effectiveness of deep learning models in classification tasks is often challenged by the quality and quantity of training data whenever they are affected by strong spurious correlations between specific attributes and target labels. This results in a form of bias affecting training data, which typically leads to unrecoverable weak generalization in prediction. This paper addresses this problem by leveraging bias amplification with generated synthetic data only: we introduce Diffusing DeBias (DDB), a novel approach acting as a plug-in for common methods of unsupervised model debiasing, exploiting the inherent bias-learning tendency of diffusion models in data generation. Specifically, our approach adopts conditional diffusion models to generate synthetic bias-aligned images, which fully replace the original training set for learning an effective bias amplifier model to be subsequently incorporated into an end-to-end and a two-step unsupervised debiasing approach. By tackling the fundamental issue of bias-conflicting training samples’ memorization in learning auxiliary models, typical of this type of technique, our proposed method outperforms the current state-of-the-art in multiple benchmark datasets, demonstrating its potential as a versatile and effective tool for tackling bias in deep learning models. Code is available at https://github.com/Malga-Vision/DiffusingDeBias

Deep neural networks have been extensively applied in the medical domain for various tasks, including image classification, segmentation, and landmark detection. However, their application is often hindered by data scarcity, both in terms of available annotations and images. This study introduces a novel application of denoising diffusion probabilistic models (DDPMs) to the landmark detection task, specifically addressing the challenge of limited annotated data in x-ray imaging. Our key innovation lies in leveraging DDPMs for self-supervised pre-training in landmark detection, a previously unexplored approach in this domain. This method enables accurate landmark detection with minimal annotated training data (as few as 50 images), surpassing both ImageNet supervised pretraining and traditional self-supervised techniques across three popular x-ray benchmark datasets. To our knowledge, this work represents the first application of diffusion models for self-supervised learning in landmark detection, which may offer a valuable pre-training approach in few-shot regimes, for mitigating data scarcity. To bolster further development and reproducibility, we provide open access to our code and pre-trained models for a variety of x-ray related applications: https://github.com/Malga-Vision/DiffusionXray-FewShot-LandmarkDetection