CLIJ: GPU-accelerated image processing for everyone

Abstract

Graphics processing units (GPU) allow image processing at unprecedented speed. We present CLIJ, a Fiji plugin enabling end-users with entry level experience in programming to benefit from GPU-accelerated image processing. Freely programmable workflows can speed up image processing in Fiji by factor 10 and more using high-end GPU hardware and on affordable mobile computers with built-in GPUs.Modern microscopy generates staggering amounts of multidimensional image data that place increasing demands on processing flexibility and efficiency. One way to speed up image processing is to exploit the parallel processing capabilities of graphics processing units (GPU).Recently, GPU-acceleration was used in specific image processing tasks such as reconstruction 1,2 , image quality determination 3 , image restoration 4 , segmentation 5 and visualisation 6 . However, in these tools, GPU code is fulfilling one specific purpose and is not intended to be reused in other contexts. By contrast, most common image processing tasks are solved by building flexible workflows consisting of simple operations in widely used tools such as ImageJ 7 and Fiji 8 . Most of these operations were however programmed at a time when GPUs were not commonly used for general purpose processing. Therefore, typical workflows consisting of core ImageJ operations do not take advantage of GPUs. To address this issue we developed a flexible and reusable platform for GPU-acceleration in Fiji.

Phosphoproteomics identifies microglial Siglec‐F inflammatory response during neurodegeneration

“…For overlap analysis, Siglec channel intensities were multiplied by copies of the Iba1 and/or MHC‐II masks that were dilated in 3D by 2 voxels to cover neighboring antibody signals. Voxel mask binary operations were performed using the And/Or/Not operations provided by ImageJ’s CLIJ plugin (Haase et al , 2020). Mask overlap was calculated as for mask intensity and area values.…”

A machine learning based approach to the segmentation of micro CT data in archaeological and evolutionary sciences

“…Implementation of the Weka algorithm, either through virtualised clusters (e.g. FIJI archipelago) or through GPU optimisation (either through CLIJ (Haase et al, 2019) or Matlab bridging) may work to ameliorate bottlenecks in processing speed. The yield in minimising user time in segmentation (as once trained, segmentation can process independently of the user) does however make the current implementation an ideal approach for the first pass segmentation of structures in archaeological and evolutionary studies.…”

What machine learning can do for developmental biology

“…The machine learning community is successful at developing methods that can be of broad use for data-intensive sciences. To grow an interdisciplinary community that would be able to fruitfully transfer knowledge between machine learning and developmental biology, we need to (1) establish wellcurated databases (Allan et al, 2012;Pierce et al, 2019), (2) establish well-identified common tasks (Regev et al, 2017;Thul et al, 2017), (3) establish computational platforms to run algorithms (McQuin et al, 2018;Haase et al, 2020) and (4) improve computational literacy in biologist communities (Ouyang et al, 2019;Caicedo et al, 2019).…”