Biological imaging has moved from 2D to 3D through the use of tomography, or of serial section/imaging, which provides spatial context in images. In tomography, a set of 2D projection images are collected and then reconstructed into a 3D volume. However, for useful information about the volume to be extracted, sub-sections of it need to be classified according to their biological purpose. Doing this manually is a time-consuming process, and current software solutions tend to be very focused, and only work well on high contrast (e.g. stained) samples. The SuRVoS (Super-Region Volume Segmentation) Workbench is a set of software tools being developed to greatly facilitate the segmentation of noisy datasets. With SuRVoS, a volume is first partitioned into Super-Regions and then interactively segmented by the user inputting training annotations. SuRVoS can then learn from and extend the annotations to the whole volume, a process that is currently five times faster than purely manual segmentation. Its initial results were recently published in the Journal of Structural Biology.
PC-12 cells with mutant Huntington's Aggregates were imaged using soft x-ray cryotomography on beamline B24 at Diamond from Diamond Light Source on Vimeo.
SuRVoS is open source, available to everyone via the Diamond GitHub.
Figure 1: Imanol Luengo and Michele Darrow using SuRVoS to analyse data from B24.
To find out more about the B24 beamline, or to discuss potential applications, please contact Principal Beamline Scientist Dr Liz Duke: liz.duke@diamond.ac.uk; for more information about SuRVoS please contact Senior Software Scientist Dr Mark Basham: mark.basham@diamond.ac.uk.
Luengo I, et al. SuRVoS: Super-Region Volume Segmentation workbench." Journal of Structural Biology (2017). DOI:10.1016/j.jsb.2017.02.007
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