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Luengo I, Darrow MC, Spink MC, Sun Y, Dai W, He CY, Chiu W, Pridmore T, Ashton AW, Duke EMH, Basham M, French AP. SuRVoS: Super-Region Volume Segmentation workbench. J Struct Biol 2017; 198:43-53. [PMID: 28246039 PMCID: PMC5405849 DOI: 10.1016/j.jsb.2017.02.007] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2016] [Revised: 02/16/2017] [Accepted: 02/20/2017] [Indexed: 01/08/2023]
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.
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Affiliation(s)
- Imanol Luengo
- School of Computer Science, University of Nottingham, Jubilee Campus, Nottingham NG8 1BB, United Kingdom; Diamond Light Source, Harwell Science & Innovation Campus, Didcot OX11 0DE, United Kingdom.
| | - Michele C Darrow
- Diamond Light Source, Harwell Science & Innovation Campus, Didcot OX11 0DE, United Kingdom.
| | - Matthew C Spink
- Diamond Light Source, Harwell Science & Innovation Campus, Didcot OX11 0DE, United Kingdom.
| | - Ying Sun
- Department of Biological Sciences, National University of Singapore, Singapore 117563, Singapore; National Center for Macromolecular Imaging, Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX 77030, USA.
| | - Wei Dai
- Department of Cell Biology and Neuroscience, and Center for Integrative Proteomics Research, Rutgers University, NJ 08901, USA.
| | - Cynthia Y He
- Department of Biological Sciences, National University of Singapore, Singapore 117563, Singapore.
| | - Wah Chiu
- National Center for Macromolecular Imaging, Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX 77030, USA.
| | - Tony Pridmore
- School of Computer Science, University of Nottingham, Jubilee Campus, Nottingham NG8 1BB, United Kingdom.
| | - Alun W Ashton
- Diamond Light Source, Harwell Science & Innovation Campus, Didcot OX11 0DE, United Kingdom.
| | - Elizabeth M H Duke
- Diamond Light Source, Harwell Science & Innovation Campus, Didcot OX11 0DE, United Kingdom.
| | - Mark Basham
- Diamond Light Source, Harwell Science & Innovation Campus, Didcot OX11 0DE, United Kingdom.
| | - Andrew P French
- School of Computer Science, University of Nottingham, Jubilee Campus, Nottingham NG8 1BB, United Kingdom.
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