Title: GPU-Accelerated Image Analysis using CLIJ and cIEsperanto

Project duration: 2022-2024

Research Area: Life Science and Medicine

Aims

In the project “GPU-Accelerated Image Analysis using CLIJ and clEsperanto” we will enable a wide target audience centered in bioimaging to make use of modern graphics processing units (GPUs) for analysing biological microscopy imaging data.

Problem

Open questions in developmental biology require state-of-the-art fluorescence microscopes to acquire in-vivo timelapse imaging data of developing cells, tissues, organoids and entire animals. As the amount of imaging data is huge, advanced techniques for processing such data is required. Technology for this exists, e.g. the Open Computing Language (OpenCL) enables processing large imaging data on GPUs. However, OpenCL and GPUs are hard to access for scientists without computational background.

Outlook

We make the processing power of GPUs accessible to a broad audience using code-generation and user-friendly graphical interfaces.

Publications

• https://www.nature.com/articles/s41592-019-0650-1
• https://link.springer.com/chapter/10.1007/978-3-030-76394-7_5
• https://www.biorxiv.org/content/10.1101/2020.11.19.386565v1
• https://chanzuckerberg.com/eoss/proposals/gpu-accelerating-fiji-and-friends-using-distributed-clij-neubias-style/
• https://chanzuckerberg.com/science/programs-resources/imaging/napari/improving-image-processing/

Team

Lead

• Dr. Robert Haase

Partners

• Dr. Stephane Rigaud (Institut Pasteur, Paris)
• Dr. Martin Jones (The Francis Crick Institute, London)
• Dr. Amy Strange (The Francis Crick Institute, London)
• Dr. Lucy Collinson (The Francis Crick Institute, London)