Iván Hidalgo-Cenalmor

@ivanhcenalmor.bsky.social

PhD researcher at Cell Migration Lab, Åbo Akademi, Turku, Finland 🇫🇮 cellmig.org

NucleiSky is now published in Journal of Cell Science @jcellsci.bsky.social! 🎉🥳 Using nuclei, it can connect microscopy images across scales and modalities, even helping microscopes automatically find the same FoV. Check out previous posts or our paper for a deeper dive: doi.org/10.1242/jcs....

Iván Hidalgo-Cenalmor@ivanhcenalmor.bsky.social · 2mo ago

Our new preprint 'NucleiSky enables cross-scale multimodal registration of microscopy data using nuclei constellations' is out! 🎉Check out @guijacquemet.bsky.social post first to learn more🤩 In this 🧵 I’ll share how we used NucleiSky in smart microscopy and how you can install and try it yourself 👇

Our new preprint 'NucleiSky enables cross-scale multimodal registration of microscopy data using nuclei constellations' is out! 🎉Check out @guijacquemet.bsky.social post first to learn more🤩 In this 🧵 I’ll share how we used NucleiSky in smart microscopy and how you can install and try it yourself 👇

Guillaume Jacquemet@guijacquemet.bsky.social · 2mo ago

Delighted to share our latest preprint "NucleiSky enables cross-scale multimodal registration of microscopy data using nuclei constellations" www.biorxiv.org/content/10.6... Code and App: github.com/CellMigratio...

🎉 Big news: our paper "Representation matters" just got accepted as a Registered Report (Stage 1) in Nature Methods! 🧬 We're building the first rigorous, preregistered benchmark of instance segmentation representations in bioimage analysis. 🧵👇 (1/3)

Examples of intermediate representations used in bottom-up instance segmentation for bioimage analysis. Shown are representative pixel- or voxel-level targets derived from instance annotations, including binary feature maps (for example, foreground, contours, centroids, and skeletons), distance-based representations (for example, horizontal/vertical offsets, radial distances, distance to boundary, and distance to center), and affinity-based representations. Here, neighbor affinities denote local connectivity predictions indicating whether adjacent pixels or voxels belong to the same object. The rows show illustrative examples from different datasets and are not intended to imply a sequential workflow or a one-to-one correspondence between representations. In the benchmarked pipelines, such intermediate
representations are subsequently converted into final object instances by downstream grouping or post-processing procedures such as watershed, clustering, or graph partitioning.

🚨Our preprint on DL for single-image super-resolution in microscopy is out!🌟 sl1nk.com/p62yedg Our benchmark recaps an adventure of learning and questioning with @ivanhcenalmor.bsky.social & @iarganda.eurosky.social 🌟 how to objectively assess SISR models & ease the choice for their use in👉 biology🔬

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Iván Hidalgo-Cenalmor@ivanhcenalmor.bsky.social · 3mo ago

Super excited to share a project I’ve worked on since my Master’s thesis, what a journey!🚵 Finally out as a Nature Methods Stage 1 Registered Report preprint doi.org/10.6084/m9.f... We benchmark deep learning-based single-image super-resolution methods for microscopy imaging. Interested? Check out 🧵

Super excited to share a project I’ve worked on since my Master’s thesis, what a journey!🚵 Finally out as a Nature Methods Stage 1 Registered Report preprint doi.org/10.6084/m9.f... We benchmark deep learning-based single-image super-resolution methods for microscopy imaging. Interested? Check out 🧵

Accuracy versus Perception: a Benchmark of Deep Learning Models for Single-Image Super-Resolution in Microscopy

Virtual super-resolution deep learning methods provide a powerful solution to overcome the physical and temporal constraints of microscopy imaging. Yet, assessing and choosing an ideal methodological strategy complicates their use in life sciences and creates a lack of trust in these methods. Here we propose an objective comparison of nine popular single-image super-resolution (SISR) models in a collection of publicly available microscopy datasets, including cell components like microtubules, endoplasmic reticulum, and actin, using confocal microscopy, SEM, SIM, SMLM and STED microscopy modalities for fixed and live-cell microscopy data. The proposed models will be assessed quantitatively with a collection of metrics in microscopy and computer vision, and qualitatively by experts in the field. The proposed models will be made accessible through open, user-friendly, containerised notebooks. This systematic assessment of SISR approaches will provide a more comprehensive understanding of these methods' performance and contribute to standardising SISR methods in microscopy.

doi.org

#IMS2026 D1 finishes with great @henriqueslab.bsky.social and @guijacquemet.bsky.social pushing the limits of live-cell super-resolution, democratizing AI and quantitative microscopy, and using all these to study cancer cell transmigration and attachment 🌟

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Estibaliz Gómez de Mariscal, PhD@gomez-mariscal.bsky.social · 5mo ago

Speaking about scalable biology, afternoon at #IMS2026 went from nano to multicellular organoids with Cecile Morlot, Helder Maiato and Catarina Brito🤩

Aging may feel gradual… but what if it’s not? In our recent paper, we tracked fish continuously from puberty until death. This gave us a unique view of how aging unfolds across the adult lifespan. 🧵

First paper as first & corresponding author, out in PLOS Computational Biology! 🎉 napariTFM brings traction force & monolayer stress microscopy into napari, accessible via GUI; open-source, validated and no coding required. 😉 Check it out 👇

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Hi Bluesky! This is my first post 🤗 I just wanted to introduce myself. My name is Iván Hidalgo, and I’m a second-year PhD student at CellMigrationLab (cellmig.org). I’ve never really been much of a social media person, but I’ll be sharing my PhD journey and the work that comes along the way 🫶🚵🚀

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