SuRVoS: Super-Region Volume Segmentation workbench

Luengo, Imanol; Darrow, Michele C.; Spink, Matthew C.; Sun, Ying; Dai, Wei; He, Cynthia Y.; Chiu, Wah; Pridmore, Tony; Ashton, Alun W.; Duke, Elizabeth M.H.; Basham, Mark; French, Andrew P.

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.

Citation

Luengo, I., Darrow, M. C., Spink, M. C., Sun, Y., Dai, W., He, C. Y., Chiu, W., Pridmore, T., Ashton, A. W., Duke, E. M., Basham, M., & French, A. P. (2017). SuRVoS: Super-Region Volume Segmentation workbench. Journal of Structural Biology, 198(1), 43-53. https://doi.org/10.1016/j.jsb.2017.02.007

• Journal Article Type: Article
• Acceptance Date: Feb 20, 2017
• Online Publication Date: Feb 27, 2017
• Publication Date: 2017-04
• Deposit Date: Mar 9, 2017
• Publicly Available Date: Mar 9, 2017
• Journal: Journal of Structural Biology
• Print ISSN: 1047-8477
• Electronic ISSN: 1095-8657
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 198
• Issue: 1
• Pages: 43-53
• DOI: https://doi.org/10.1016/j.jsb.2017.02.007
• Keywords: Interactive segmentation; Hierarchical segmentation; Super-Regions; Semi-supervised learning; Cryo soft X-ray tomography; Cryo electron tomography
• Public URL: https://nottingham-repository.worktribe.com/output/844187
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S1047847717300308?via%3Dihub
• Additional Information: This article is maintained by: Elsevier; Article Title: SuRVoS: Super-Region Volume Segmentation workbench; Journal Title: Journal of Structural Biology; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.jsb.2017.02.007; Content Type: article; Copyright: © 2017 Diamond Light Source. Published by Elsevier Inc.
• Contract Date: Mar 9, 2017