Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench

Abstract

Segmentation is the process of isolating specific regions or objects within an imaged volume, so that further study can be undertaken on these areas of interest. When considering the analysis of complex biological systems, the segmentation of three-dimensional image data is a time consuming and labor intensive step. With the increased availability of many imaging modalities and with automated data collection schemes, this poses an increased challenge for the modern experimental biologist to move from data to knowledge. This publication describes the use of SuRVoS Workbench, a program designed to address these issues by providing methods to semi-automatically segment complex biological volumetric data. Three datasets of differing magnification and imaging modalities are presented here, each highlighting different strategies of segmenting with SuRVoS. Phase contrast X-ray tomography (microCT) of the fruiting body of a plant is used to demonstrate segmentation using model training, cryo electron tomography (cryoET) of human platelets is used to demonstrate segmentation using super- and megavoxels, and cryo soft X-ray tomography (cryoSXT) of a mammalian cell line is used to demonstrate the label splitting tools. Strategies and parameters for each datatype are also presented. By blending a selection of semi-automatic processes into a single interactive tool, SuRVoS provides several benefits. Overall time to segment volumetric data is reduced by a factor of five when compared to manual segmentation, a mainstay in many image processing fields. This is a significant savings when full manual segmentation can take weeks of effort. Additionally, subjectivity is addressed through the use of computationally identified boundaries, and splitting complex collections of objects by their calculated properties rather than on a case-by-case basis.

Optimising complementary soft tissue synchrotron X-ray microtomography for reversibly-stained central nervous system samples

“…With SRμCT scanning parameters optimised and artefacts corrected, various macro- and micro-features could be discerned within spinal cord samples. Segmentation of these features was achieved with shallow machine learning through the SuRVoS Workbench 38 , 39 . Features within unbinned tomograms (Fig.…”

“…Tomograms were matched to histology images by re-slicing and thickness adjusting in IMOD 67 . Spinal cord, white & grey matter and vasculature were segmented with the SuRVoS Workbench 38 , 39 . 3D renders were made in Avizo 9.4 (FEI Systems, Inc., USA) at I13-2 68 .…”

Cryo-soft X-ray tomography: using soft X-rays to explore the ultrastructure of whole cells

“…Manual segmentation can be, by far, the most time-consuming step in a cryo-SXT experiment and it is generally regarded as somewhat objective and variable [88]. Recent advances in data representation and segmentation have been introduced at Diamond Light Source through the SuRVoS Workbench, which uses minimal manual training inputs to assign voxels in the volume to user-defined classes [19,89]. Once cellular features have been segmented and classified, further analysis of size, shape and localisation within the context of a whole cell can lead to biological understanding.…”

Okapi-EM – a napari plugin for processing and analysing cryogenic serial FIB/SEM images

“…For instance, small translational movements between images within the stack caused by stage and/or sample movement are often observed as misalignment between images; while compensatory functions exist within the instrument it is not always possible to correct on-the-fly. Therefore, these must be compensated for, otherwise volumetric segmentation (14,15) and computational counting tools like connected components algorithms will fail or struggle to succeed. Additionally, SEM images of biological samples often contain artefacts caused by charging around insulating substances such as lipids (4,11) .…”