Biology subjects

Gonzalez-Marfil, A.

Publications and source records attributed to Gonzalez-Marfil, A..

3 recordsLinked to original sources

bioRxiv2025

DINOSim: Zero-Shot Object Detection and Semantic Segmentation on Electron Microscopy Images

We present DINOSim, a novel method for detecting and segmenting objects in microscopy images without the need for large annotated datasets or additional training. DINOSim builds on the pretrained DINOv2 image encoder, which captures semantic information from images. By comparing the encoders features of images patches to those of a user-selected reference, DINOSim generates pseudo-labels that guide object detection and segmentation. Subsequently, a k-nearest neighbors framework is then used to refine predictions across new images. Our experiments show that DINOSim can effectively identify and segment previously unseen objects in diverse microscopy datasets, offering performance comparable to supervised approaches while avoiding the need for costly manual labeling. We also investigate how different choices of user prompts selection and model size affect accuracy and generalization. To make the method widely accessible, we provide an open-source Napari plugin (github.com/AAitorG/napari-DINOSim), enabling researchers to easily apply DINOSim to their own data. Overall, DINOSim offers a fast, flexible and practical solution for bioimage analysis, particularly valuable in resource-constrained settings.

bioinformatics↗

bioRxiv2024

Self-supervised Vision Transformers for image-to-image labeling: a BiaPy solution to the LightMyCells Challenge

Fluorescence microscopy plays a crucial role in cellular analysis but is often hindered by phototoxicity and limited spectral channels. Label-free transmitted light microscopy presents an attractive alternative, yet recovering fluorescence images from such inputs remains difficult. In this work, we address the Cell Painting problem within the LightMyCells challenge at the International Symposium on Biomedical Imaging (ISBI) 2024, aiming to predict optimally focused fluorescence images from label-free transmitted light inputs. Leveraging advancements self-supervised Vision Transformers, our method overcomes the constraints of scarce annotated biomedical data and fluorescence microscopys drawbacks. Four specialized models, each targeting a different organelle, are pretrained in a self-supervised manner to enhance model generalization. Our method, integrated within the open-source BiaPy library, contributes to the advancement of image-to-image deep-learning techniques in cellular analysis, offering a promising solution for robust and accurate fluorescence image prediction from label-free transmitted light inputs. Code and documentation can be found at https://github.com/danifranco/BiaPy and a custom tutorial to reproduce all results is available at https://biapy.readthedocs.io/en/latest/tutorials/image-to-image/lightmycells.html.

bioinformatics↗

bioRxiv2024

BiaPy: A unified framework for versatile bioimage analysis with deep learning

BiaPy is an open-source library and application that streamlines the use of common deep learning approaches for bioimage analysis. Designed to simplify technical complexities, it offers an intuitive interface, zero-code notebooks, and Docker integration, catering to both users and developers. While focused on deep learning workflows for 2D and 3D image data, it enhances performance with multi-GPU capabilities, memory optimization, and scalability for large datasets. Although BiaPy does not encompass all aspects of bioimage analysis, such as visualization and manual annotation tools, it empowers researchers by providing a ready-to-use environment with customizable templates that facilitate sophisticated bioimage analysis workflows.

bioinformatics↗