RT @LabMri: Last week at the @ISMRMIberian Chapter, our teammate @maxeggl presented groundbreaking work on using simulation-based inference….

RT @LabMri: 📢 Mark your calendars! On July 4, our very own @eespinos97 takes the stage at the @ISMRMIberian!. She’ll dive into how male and….

However, this time I finally managed! I would like to thank @si_de_santis @LabMri for the incredible support, which I think was exactly the difference that allowed me to succeed this year. Now on to more science and research at the intersection between neuroscience and ML!.

With a bit of delay (it’s taken a while to process), I am happy to announce that I was awarded a #RyC2024 fellowship this year! One of the most prestigious research fellowships in Spain, I have been working towards this goal for a long time (with many rejections along the way)!.

9/.💡 SpyDen bridges the gap between molecular-resolution imaging and user-friendly analysis. If you’re doing fluorescence imaging of neurons—check it out. Reproducible, customizable, and made for the community. #OpenScience #Neuroinformatics #Microscopy.

8/.📦 Get it here:.Code: Compiled executables: Documentation & tutorials included. This is an ongoing project so we’d love feedback from the community!.

7/.🖥️ Works out of the box:.• For coders & non-coders.• Trained networks available.• No need to install complex dependencies.• Built for transparency and customization.

6/.🧪 We validated SpyDen against expert annotations across diverse datasets and use cases. It performs reliably, making it suitable for both exploratory research and reproducible pipelines.

5/.🧰 What can SpyDen do?.• Detect neurites & synapses.• Track fluorescent puncta over time.• Analyze intensity & localization.• Export data in standard formats.• All with a GUI and video tutorials for onboarding.

4/.✅ Enter SpyDen:.A Python-based platform designed to address this with 3 core goals:.1️⃣ Easy to use for multiple tasks.2️⃣ Fully open-source, with open data formats.3️⃣ Editable annotations, robust across resolutions.

3/.❌ Most workflows today:.• Multiple software packages.• Custom scripts.• Manual annotation.• Poor reproducibility.• Limited scalability.And many AI tools are not general-purpose, not open, or hard to modify.

2/.🔍 Motivation:.Studying learning and memory means understanding molecules inside axons, dendrites, and synapses. Modern microscopy can detect single molecules—.…but analyzing those images? Still often manual, fragmented, or semi-automated.

🧵1/Just published: SpyDen, developed with @TTchumatchenko, @SurbhitWagle, J. Filling, T. Chater & Y. Goda — an open-source Python tool for analyzing 2D microscopy time-series of neurons. GUI-based, robust, and validated by experts. 🔗 Read on for more: 👇.

RT @LabMri: 🗓️ Save the date!. DeSantis Lab will be at the @ISMRMIberian Chapter Meeting in Barcelona, July 3–4 🇪🇸. We’ll be sharing our la….

RT @SENC_: 📰 "Menos tiempo, mejor diagnóstico: cuando la IA alivia las listas de espera en resonancia magnética". 🖊️ @maxeggl @si_de_santi….

RT @LabMri: Our new article in @Conversation_E! .We show how AI can reduce brain MRI scan time by 90% — without losing accuracy. Faster, mo….

Our pop science article (in Spanish 🇪🇸) breaks it down for everyone: And check out the preprint too: 🧵.

Why this matters:.✔️ Shorter, more comfortable scans.✔️ More patients can get high-quality imaging.✔️ Older, noisy datasets can now be rescued.✔️ Clinics can finally use advanced dw-MRI tools.All without needing to share or store sensitive patient data. (6/n).

We trained SBI to estimate key diffusion parameters from fewer MRI measurements — and it worked. ✨ 90% faster scans.✨ High accuracy preserved.✨ Robust even with noisy data.(5/n).

Enter: Simulation-Based Inference (SBI) — a new AI approach that flips the script. Instead of training on huge amounts of real patient data, SBI learns from simulated data and can handle noisy, sparse measurements much more efficiently. #SBI (4/n).