Abstract
Fluorescence microscopy faces challenges in resolution, phototoxicity, and anisotropic artifacts. The Fuse My Cells challenge, organized by France-BioImaging, aims to develop deep learning models that predict fused 3D volumes from single-view acquisitions, reducing phototoxic exposure while enhancing resolution. In this work, we benchmark state-of-the-art super-resolution models, including DFCAN, RCAN-3D, UNETR, and a 3D-adapted RCAN-it, evaluating their performance on the Fuse My Cells challenge dataset, which encompasses 802 3D light-sheet microscopy images. A novel training strategy prioritizing high-discrepancy regions optimizes efficiency and improves reconstruction accuracy. Our findings suggest that super-resolution models can not fully reconstruct the information on those image areas with minimum signal information. Code and documentation can be found at https://github.com/danifranco/BiaPy.
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.