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SuRVoS: Super-Region Volume Segmentation Workbench

DOI: 10.1016/j.jsb.2017.02.007 DOI Help

Authors: Imanol Luengo (University of Nottingham; Diamond Light Source) , Michele C. Darrow (Diamond Light Source) , Matthew C. Spink (Diamond Light Source) , Ying Sun (National University of Singapore; Baylor College of Medicine) , Wei Dai (Rutgers University) , Cynthia Y. He (National University of Singapore) , Wah Chiu (Baylor College of Medicine) , Tony Pridmore (University of Nottingham) , Alun W. Ashton (Diamond Light Source) , Elizabeth M. H. Duke (Diamond Light Source) , Mark Basham (Diamond Light Source) , Andrew P. French (University of Nottingham)
Co-authored by industrial partner: No

Type: Journal Paper
Journal: Journal Of Structural Biology

State: Published (Approved)
Published: February 2017

Open Access Open Access

Abstract: Segmentation of biological volumes is a crucial step needed to fully analyse their scientific content. Not having access to convenient tools with which to segment or annotate the data means many biological volumes remain under-utilised. Automatic segmentation of biological volumes is still a very challenging research field, and current methods usually require a large amount of manually-produced training data to deliver a high-quality segmentation. However, the complex appearance of cellular features and the high variance from one sample to another, along with the time-consuming work of manually labelling complete volumes, makes the required training data very scarce or non-existent. Thus, fully automatic approaches are often infeasible for many practical applications. With the aim of unifying the segmentation power of automatic approaches with the user expertise and ability to manually annotate biological samples, we present a new workbench named SuRVoS (Super-Region Volume Segmentation). Within this software, a volume to be segmented is first partitioned into hierarchical segmentation layers (named Super-Regions) and is then interactively segmented with the user's knowledge input in the form of training annotations. SuRVoS first learns from and then extends user inputs to the rest of the volume, while using super-regions for quicker and easier segmentation than when using a voxel grid. These benefits are especially noticeable on noisy, low-dose, biological datasets.

Journal Keywords: Interactive segmentation; Hierarchical segmentation; Super-regions; Semi-supervised learning; Cryo soft x-ray tomography; Cryo electron tomography

Subject Areas: Information and Communication Technology, Technique Development, Biology and Bio-materials


Instruments: B24-Cryo Soft X-ray Tomography

Documents:
1-s2.0-S1047847717300308-main.pdf