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

Abstract

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 microscopy’s 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.

Daniel Franco-Barranco

Postdoctoral Researcher

My primary focus is on the development of deep learning solutions for the segmentation of organelles in large-scale and multimodal electron microscopy images.

Aitor González-Marfil

PhD Student

My research focuses on generalizable deep learning methods for computer vision.

Ignacio Arganda-Carreras

Ikerbasque Research Associate Professor

My research interests include image processing, computer vision, and deep learning for biomedical applications.