Speaker
Dr
Christian Voss
(DESY Hamburg)
Description
We are exploiting possible applications of artificial intelligence at
the German electron synchrotron (DESY) in Hamburg, in particular in the
field of photon science. Our current focus is on the use of
convolutional neural networks applied to 2D and 3D image analysis for
life science.
We will present successful applied semantic segmentation of volumetric
3D synchrotron radiation microcomputed tomography (SRμCT) data with a
U-Net. We have trained a convolutional neural network to segment
biodegenerable bone implants (screws) and degeneration products from
bone and background. The results obtained significantly outperform the
previously used semi-automatic segmentation procedure in terms of
accuracy and has successfully been applied to more than 100 rather
heterogeneous datasets. Remarkably the performance of the U-Net
segmentation is considerably better than the experts segmentation that
has been used for training.
In addition to our ongoing work for instance segmentation (SRμCT) in the
context of material science, object detection and classification for
cryo electron tomography
will be introduced. With a combination of a U-Net and a simple
convolutional network for object classification, membrane protein
complexes are identified in CryoEM tomograms, for subsequent subtomogram
averaging. The machine learning efforts at DESY-IT also include the
development of a classification/filter method for XFEL SFX diffraction data.
Primary authors
Dr
Christian Voss
(DESY Hamburg)
Dr
Philipp Heuser
(DESY)
