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Education Strategies of Imaging Cores

Posted by Daniel Doucet, on 1 April 2024

Over the past two decades, high profile research institutions have come to rely on distinct core facilities for much of their data and image analysis. The benefits of the newest technologies are often softened by the costs of purchasing and maintaining such equipment for regular use. For microscopy and imaging, it only made sense that

The Revenge of Image.sc LIVE! around the world - the live bioimage analysis helpdesk is returning to a time zone near you!

Posted by Georgina Fletcher, on 25 March 2024

Community surveys often point to the biggest bottleneck in excellent bioimaging science being image analysis. That’s why the RMS DAIM committee and their friends across the world are putting on another event to highlight the fantastic image.sc forum. All the info you need is here: Image.sc LIVE around the world! – Announcements – Image.sc Forum and in the poster

Image Analysis course with Fiji/ImageJ

Posted by Lior Pytowski, on 20 March 2024

An online course introducing you to the basics of image analysis, including automatic segmentation, colocalisation, denoising, 3D, etc. Two sessions, happening in May and June 2024.

Will your algorithm be the best for this new image data challenge?

Posted by France-BioImaging, on 26 February 2024

In order to answer image data analysis demands, France-BioImaging is launching its first data machine learning competition: welcome to the Light My Cells challenge! The Light My Cells challenge aims at contributing to the development of new image-to-image ‘deep-label’ methods in the fields of biology and microscopy. Basically, the goal is to predict the best-focused output-images of several organelles

Image Analysis course with Fiji/ImageJ

Posted by Lior Pytowski, on 20 February 2024

An online course introducing you to the basics of image analysis, including automatic segmentation, colocalisation, denoising, 3D, etc.

Enhancing Global Access: interview with CZI grantee Mahmoud Maina

Posted by Mariana De Niz, on 24 January 2024

BIOIMAGING NETWORK IN WEST AFRICA Mahmoud Maina is an Associate Professor at Yobe State University in Nigeria and Independent Research Fellow at the University of Sussex. He is the founder and Director of the biomedical Science Research and training Centre (BioRTC) at Yobe State University. His project aims to establish a West African Bioimaging Network

Enhancing Global Access: interview with CZI grantee Cristina Guatimosim

Posted by Mariana De Niz, on 24 January 2024

BIOIMAGING NETWORK FOR THE ADVANCEMENT OF BIOMEDICAL RESEARCH Cristina Guatimosim is leading the Bioimaging Network of Minas Gerais in Brazil (BioIMG Net). This is a pioneering model that aims to increase accessibility through several initiatives: acquisition of state-of-the-art instruments, capacity building, sponsoring workshops and other training opportunities, and engaging with public schools and the general

Enhancing Global Access: interview with CZI grantee Mark Scimone

Posted by Mariana De Niz, on 24 January 2024

A NEW PROTOTYPE FOR IMAGING CORE FACILITIES As his project for the Cycle 1 imaging scientist call, Mark Scimone proposed a new prototype of what the standard imaging core facility could be. This includes facilitating collaborations between vendors, researchers and cores; generating structured microscopy and optics education courses which also introduce new research technologies; and recruiting new

Enhancing Global Access: interview with CZI grantee Christian Tischer

Posted by Mariana De Niz, on 24 January 2024

SUPPORTING IMAGE ANALYSIS AND COMPUTATIONAL INFRASTRUCTURE Christian Tischer is a specialist in microscopy and image analysis. He worked for 10 years at EMBL’s Advanced Light Microscopy Facility, and since 2018 leads EMBL’s Center for Bioimage Analysis. His project is multi-pronged, including driving forward the establishment of standard file formats for imaging data, developing a unified

Enhancing Global Access: interview with CZI grantee Paul Hernandez

Posted by Mariana De Niz, on 24 January 2024

ADVANCING BIOIMAGING CORE SERVICES WITH ARTIFICIAL INTELLIGENCE Paul Hernandez is a mathematician who develops algorithms for image analysis. Currently, he is pioneering the use of machine learning and deep learning techniques for image analysis. He is an imaging scientist awardee of the CZI cycle 2 call. His project aims to develop accurate and high-throughput AI

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Microscopy-related articles from our journals

HCR spectral imaging: 10-plex, quantitative, high-resolution RNA and protein imaging in highly autofluorescent samples Development 2024 151: dev202307
filoVision – using deep learning and tip markers to automate filopodia analysis J Cell Sci 2024 137: jcs261274
C omputational tools for quantifying actin filament numbers, lengths, and bundling Biology Open 2024 13: bio.060267

Microscopy-related preprint highlights

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