HPC enables efficient 3D membrane segmentation in electron tomography

Electron Tomography (ET) is a powerful three-dimensional (3D) imaging technique used in structural biology and biomedicine to allow the visualization of the interior of cells at close-to-molecular resolution. Interpretation of the 3D volumes in ET is usually challenging due to the complexity of the cellular environment, noise conditions and other factors. Automated segmentation methods focused on membranes of the cells and organelles greatly facilitate visualization and interpretation of the 3D volumes. However, they are typically computationally expensive and spend significant processing time on standard computers. In this work, we introduce efficient implementations of one of the methods most commonly used in the ET field for membrane segmentation. They were developed by using High Performance Computing (HPC) techniques to make the most of modern CPU-based and GPU-based computing platforms. A thorough evaluation of the performance on state-of-the-art machines was carried out. The HPC implementations succeed in achieving remarkable speedups, which are around 100x on GPUs, and making it possible to process large 3D volumes in the order of seconds or a few minutes.