Virginie Uhlmann

Shield-4i enables high-throughput, subcellular-resolution imaging of whole-mount structures to study multiscale information flow in multicellular systems.

This study creates a comprehensive spatial atlas of the human ovarian cortex, revealing molecular mechanisms of primordial follicle activation and follicle niche organization.

The paper introduces SI-SimCLR, a self-supervised learning framework for reconstructing cellular phenotypic landscapes from microscopy data without segmentation or annotation, revealing substate organization and connections in astrocytes.

Fractal introduces a task specification and platform for scalable, reproducible OME-Zarr-native bioimage analysis workflows, demonstrating reproducibility across diverse imaging data and clinical settings.

This study investigates residual polarization ellipticity in waveplate-compensated polarization-resolved SHG microscopy and identifies femtosecond laser spectral bandwidth as a potential cause.

DeepTangleCrawl, DeepTangle retrained for crawling C. elegans, skeletonises more coiled and overlapping worms than Tierpsy, Omnipose or a PAF tracker, with longer tracks and larger screen effect sizes than Tierpsy.

This study integrates single-cell and spatial transcriptomics with histopathology to identify cellular and molecular changes in lung alveolar damage during COVID-19 progression.

The paper maps the spatial organization of the human thymus across developmental stages using a multimodal atlas and identifies key cellular and molecular features of T cell development.

The paper shows that stochastic division timing and cell mechanics contribute to robust embryo patterning and morphogenesis in mammals.

This paper introduces VascuMap, a deep-learning tool for label-free microvessel segmentation and spatial analysis, achieving high accuracy and enabling comprehensive examination of vascular networks.

The paper introduces a simulation pipeline to generate structurally variable synthetic NPCs for benchmarking image analysis methods and understanding NPC variability in SMLM data.

This study develops an engineered uterus-like microenvironment to study mouse embryo implantation, revealing coordination between trophoblast migration and embryo growth.

The paper introduces an unsupervised method to extract morphological features from 3D EM data, enabling cell type and tissue organization analysis in a segmented worm.

SplineDist improves cell segmentation by using spline curves instead of star-convex polygons, achieving comparable quality with a smaller network and better handling of complex shapes.

This paper introduces a convolutional neural network to predict continuous parametric representations of biological object outlines, tested on cell nucleus images from a public dataset.

The paper introduces FlyLimbTracker, a method for semi-automatically tracking leg segments in unmarked Drosophila, showing robustness across video resolutions and behaviors.