Pick Yolo

Pick Yolo

In collaboration with the Center for Structural Systems biology, Helmholtz Imaging has developed and trained a convolutional neural network for the picking of instances of proteins in cryoelectrontomograms (CryoET). The so picked instances are subsequently used to reconstruct a 3D structure of the proteins using subtomogram averaging methods. The novel picking methods exploit the well known YOLO object detection algorithm and thus can analyse an entire tomogram in about 1 second.

Other Collaborations


Connecting membrane pores and production parameters via machine learning (COMPUTING)

Isoporous block-copolymer membranes play a fundamental role in the filtration of liquids and can, for example, be used to purify drinking water. Despite recent progress in understanding the membrane formation process, finding suitable production parameters for a given precursor material (such as polymers of a certain length) still occurs in a trial-and-error fashion, wasting materials, […]

Optimizing electrode geometry and surface for hydrogen production

Electrolysis of water into oxygen and hydrogen is a cornerstone of modern energy storage, electric mobility and the transition towards a net-zero-emissions industry. Maximizing the efficiency of this technology is key to its economically viable wide scale adoption. One approach to both reduce the costs and improve the overall efficiency is to enhance the bubble […]

Extracting clinically relevant parameters from real-time MRI images of fontan hearts

Obtaining accurate segmentations of the heart in real-time MRI allows a more realistic view on clinically relevant parameters, such as the stroke volume. Cardiac real-time MRI can assess diastolic filling under breath maneuvers or other cardiac load situations which potentially enhances diagnostics other than CINE breath hold cardiac MRI. Real-time MRI allows rapid acquisitions during […]