Estibaliz Gómez de Mariscal

Machine-Learning Guided Microscopy
Started on October, 2021
Google Scholar

Overview: I'm interested in understanding cell-level biology using bioimage analysis. My research centers on facing the challenges when applying machine-learning techniques to microscopy images and on contributing to biological discoveries with it. Previously, I developed methods to process TEM images and phase-contrast time-lapse movies to contribute to the characterization of cancer cell motility. I have also conceived new biostatistical approaches to analyse big data. I'm also one of the creators of deepImageJ, an environment to bridge deep-learning to ImageJ. A crucial part of my work and dedication is to make computational tools accessible (open and user-friendly) and reusable, and train non-experts to benefit from them. As a postdoc in Ridardo's lab I'll be learning (a lot) of super-resolution microscopy, and building novel machine-learning methodologies applied to it.

Research themes with our lab



Mike Heilemann

Single Molecule Biophysics, Goethe University Frankfurt, Germany

Publications with our group (see more on Google Scholar)

BioImage Model Zoo: A Community-Driven Resource for Accessible Deep Learning in BioImage Analysis
Wei Ouyang, Fynn Beuttenmueller, Estibaliz Gómez-de-Mariscal, Constantin Pape, Tom Burke, Carlos Garcia-López-de-Haro, Craig Russell, Lucía Moya-Sans, Cristina de-la-Torre-Gutiérrez, Deborah Schmidt, Dominik Kutra, Maksim Novikov, Martin Weigert, Uwe Schmidt, Peter Bankhead, Guillaume Jacquemet, Daniel Sage, Ricardo Henriques, Arrate Muñoz-Barrutia, Emma Lundberg, Florian Jug, Anna Kreshuk
Published in bioRxiv, June 2022 (see preprint)