Volume segmentation and analysis of biological materials using SuRVoS (Super-region Volume Segmentation) workbenchTools Darrow, Michele C., Luengo, Imanol, Basham, Mark, Spink, Matthew C., Irvine, Sarah, French, Andrew P., Ashton, Alun W. and Duke, Elizabeth M.H. (2017) Volume segmentation and analysis of biological materials using SuRVoS (Super-region Volume Segmentation) workbench. Journal of Visualized Experiments (126). e56162/1-e56162/13. ISSN 1940-087X Full text not available from this repository.
Official URL: https://doi.org/10.3791/56162
AbstractSegmentation 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.
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