Section: New Software and Platforms

DeepFinder

Deep learning for macromolecule identification within 3D cellular cryo-electron tomograms

Keywords: Image analysis - Deep learning - Cryo-electron microscopy - Object detection

Functional Description: DeepFinder is a computational approach that uses artificial neural networks to accurately and jointly localize multiple types and/or states of macromolecules in 3D cellular cryo-electron tomograms. DeepFinder leverages deep learning and outperforms the commonly-used template matching method on ideal data. On synthetic image data (SHREC 2019 challenge), DeepFinder is very fast and produces superior detection results when compared to other competitive deep learning methods, especially on small macromolecules. On experimental cryo-ET data depicting ribosomes, the detection results obtained by DeepFinder are consistent with expert annotations. We have got a high overlap of 86% and a similar structure resolution determined by subtomogram averaging.

• Participants: Emmanuel Moebel, Antonio Martinez and Charles Kervrann

• Partners: Max Planck Institute Martinsried - Fondation Fourmentin-Guilbert

• Contact: Charles Kervrann

• URL: https://gitlab.inria.fr/serpico/deep-finder