The Helmholtz-Zentrum Hereon is operating imaging beamlines for X-ray tomography (P05 IBL, P07 HEMS) for academic and industrial users at the synchrotron radiation source PETRA III at DESY in Hamburg, Germany. The high X-ray flux density and coherence of synchrotron radiation enables high-resolution in situ/operando/vivo tomography experiments and provides phase contrast, respectively. Large amounts of 3D/4D data are collected that are difficult to process and analyze. Here, we report on the application of machine learning for image segmentation including a guided interactive framework, multimodal data analysis (virtual histology), image enhancement (denoising), and self-supervised learning for phase retrieval.
KEYWORDS: Tomography, Image segmentation, Monte Carlo methods, Performance modeling, Bone, Process modeling, Data acquisition, Analytical research, Web services, Synchrotron radiation
The Helmholtz-Zentrum Hereon is operating several tomography end stations at the beamlines P05 and P07 of the synchrotron radiation facility PETRA III at DESY in Hamburg, Germany. Attenuation and phase contrast imaging techniques are provided as well as sample environments for in situ/operando/vivo experiments for applications in biology, medicine, materials science, etc. Very large and diverse data sets with varying spatiotemporal resolution, noise levels and artifacts are acquired which are challenging to process and analyze. Here we report on an active learning approach for the semantic segmentation of tomography data using a guided and interactive framework, and evaluate different acquisition functions for the selection of images to be annotated in the iterative process.
Sebastian Schmelzle, Michael Heethoff, Vincent Heuveline, Philipp Lösel, Jürgen Becker, Felix Beckmann, Frank Schluenzen, Jörg Hammel, Andreas Kopmann, Wolfgang Mexner, Matthias Vogelgesang, Nicholas Tan Jerome, Oliver Betz, Rolf Beutel, Benjamin Wipfler, Alexander Blanke, Steffen Harzsch, Marie Hörnig, Tilo Baumbach, Thomas van de Kamp
Beamtime and resulting SRμCT data are a valuable resource for researchers of a broad scientific community in life sciences. Most research groups, however, are only interested in a specific organ and use only a fraction of their data. The rest of the data usually remains untapped. By using a new collaborative approach, the NOVA project (Network for Online Visualization and synergistic Analysis of tomographic data) aims to demonstrate, that more efficient use of the valuable beam time is possible by coordinated research on different organ systems. The biological partners in the project cover different scientific aspects and thus serve as model community for the collaborative approach. As proof of principle, different aspects of insect head morphology will be investigated (e.g., biomechanics of the mouthparts, and neurobiology with the topology of sensory areas). This effort is accomplished by development of advanced analysis tools for the ever-increasing quantity of tomographic datasets. In the preceding project ASTOR, we already successfully demonstrated considerable progress in semi-automatic segmentation and classification of internal structures. Further improvement of these methods is essential for an efficient use of beam time and will be refined in the current NOVAproject. Significant enhancements are also planned at PETRA III beamline p05 to provide all possible contrast modalities in x-ray imaging optimized to biological samples, on the reconstruction algorithms, and the tools for subsequent analyses and management of the data. All improvements made on key technologies within this project will in the long-term be equally beneficial for all users of tomography instrumentations.
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