Depth-enhanced high-throughput microscopy by compact PSF engineering

Autor: Nadav Opatovski, Elias Nehme, Noam Zoref, Ilana Barzilai, Reut Orange Kedem, Boris Ferdman, Paul Keselman, Onit Alalouf, Yoav Shechtman

Publikováno v: Nature Communications, Vol 15, Iss 1, Pp 1-11 (2024)

Abstract High-throughput microscopy is vital for screening applications, where three-dimensional (3D) cellular models play a key role. However, due to defocus susceptibility, current 3D high-throughput microscopes require axial scanning, which lowers

Externí odkaz: https://doaj.org/article/5c0fb5cf44f0490bab7ae17cf4a4eb24

Democratising deep learning for microscopy with ZeroCostDL4Mic

Autor: Lucas von Chamier, Romain F. Laine, Johanna Jukkala, Christoph Spahn, Daniel Krentzel, Elias Nehme, Martina Lerche, Sara Hernández-Pérez, Pieta K. Mattila, Eleni Karinou, Séamus Holden, Ahmet Can Solak, Alexander Krull, Tim-Oliver Buchholz, Martin L. Jones, Loïc A. Royer, Christophe Leterrier, Yoav Shechtman, Florian Jug, Mike Heilemann, Guillaume Jacquemet, Ricardo Henriques

Publikováno v: Nature Communications, Vol 12, Iss 1, Pp 1-18 (2021)

Deep learning methods show great promise for the analysis of microscopy images but there is currently an accessibility barrier to many users. Here the authors report a convenient entry-level deep learning platform that can be used at no cost: ZeroCos

Externí odkaz: https://doaj.org/article/71c9afaf6b9849e787693c838589c312

Learning Optimal Multicolor PSF Design for 3D Pairwise Distance Estimation

Autor: Ofri Goldenberg, Boris Ferdman, Elias Nehme, Yael Shalev Ezra, Yoav Shechtman

Publikováno v: Intelligent Computing, Vol 2022 (2022)

Measuring the 3-dimensional (3D) distance between 2 spots is a common task in microscopy, because it holds information on the degree of colocalization in a variety of biological systems. Often, the 2 spots are labeled with 2 different colors, as each

Externí odkaz: https://doaj.org/article/a09fb805760648c091ff91e3f38e8297

Single-Particle Diffusion Characterization by Deep Learning

Autor: Eran Perlson, Elias Nehme, Lucien E. Weiss, Naor Granik, Yoav Shechtman, Yael Roichman, Maayan Levin, Michael Chein

Publikováno v: Biophys J

Diffusion plays a crucial role in many biological processes including signaling, cellular organization, transport mechanisms, and more. Direct observation of molecular movement by single-particle-tracking experiments has contributed to a growing body

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d0e73eb77d66221230348fde45f6b998
https://doi.org/10.1016/j.bpj.2019.06.015

Democratising deep learning for microscopy with ZeroCostDL4Mic

Autor: Pieta K. Mattila, Lucas von Chamier, Yoav Shechtman, Elias Nehme, Guillaume Jacquemet, Johanna Jukkala, Alexander Krull, Daniel Krentzel, Loic Royer, Tim-Oliver Buchholz, Mike Heilemann, Christophe Leterrier, Martina Lerche, Romain F. Laine, Ricardo Henriques, Florian Jug, Sara Hernández-Pérez, Martin L. Jones, Eleni Karinou, Ahmet Can Solak, Christoph Spahn, Seamus Holden

Publikováno v: Nature Communications
Nature Communications, Nature Publishing Group, 2021, 12, ⟨10.1038/s41467-021-22518-0⟩
Nature Communications, Vol 12, Iss 1, Pp 1-18 (2021)
Nature Communications, 2021, 12, ⟨10.1038/s41467-021-22518-0⟩

Deep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fuelled by DL technology, the need to access powerful and compatible resources

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::020f134a7f24e39c4432fd2895e9d76b
https://hal-amu.archives-ouvertes.fr/hal-03233449/document