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Correlative soft tissue synchrotron microtomography: Sample preparation, imaging, reconstruction and segmentation methods

DOI: 10.22443/rms.mmc2021.177 DOI Help

Authors: Andrew Bodey (Diamond Light Source) , Merrick Strotton (King's College London) , Win Tun (Diamond Light Source) , Alexey V. Buzmakov (Institute of Photon Technologies of Federal Scientific Research Centre, Crystallography and Photonics of Russian Academy of Sciences) , Victoria Gulimova (Research Institute of Human Morphology) , Kazimir Wanelik (Diamond Light Source) , Michele Darrow (Diamond Light Source) , Elizabeth Bradbury (King's College London) , Christoph Rau (Diamond Light Source)
Co-authored by industrial partner: No

Type: Conference Paper
Conference: Microscience Microscopy Congress (MMC2021)
Peer Reviewed: Yes

State: Published (Approved)
Published: July 2021
Diamond Proposal Number(s): 12538 , 14907 , 23866

Open Access Open Access

Abstract: Thousands of soft tissue microtomography experiments are conducted at synchrotrons around the world each year, and the quality of results varies widely. Soft biological tissues pose a particular challenge for synchrotron tomography, owing to poor contrast and susceptibility to deformation and beam damage artefacts. The rationale behind the choice of sample preparation methods, imaging parameters and reconstruction strategy is not always reported in articles, and so we conducted a systematic investigation of these aspects of experimental design for central nervous system samples. Computational segmentation can be particularly challenging for soft-tissue tomograms, and so we demonstrate the use of supervoxel-based machine-learning segmentation of our data.

Journal Keywords: Soft tissue; central nervous system; spinal cord; synchrotron; tomography; CT; reconstruction; machine learning; segmentation

Subject Areas: Physics, Biology and Bio-materials, Technique Development

Instruments: I13-2-Diamond Manchester Imaging

Added On: 27/10/2021 14:43

Discipline Tags:

Information & Communication Technologies Artificial Intelligence Life Sciences & Biotech Technique Development - Life Sciences & Biotech

Technical Tags:

Imaging Tomography