SuRVoS: Super-Region Volume Segmentation workbench
Open accessAbstract
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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Machine learning methods for interactive segmentation can achieve higher segmentation accuracy with fewer user interactions, but are limited by hand-crafted features that depend on the user's experience.
“Machine learning methods [6], [37], [38] have been proposed to learn from user interactions. They can achieve higher segmentation accuracy with fewer user interactions. However, they are limited by hand-crafted features that depend on the user's experience.”
From DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation (2019) · cites this paper as [38]
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DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
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- Authors
- 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. H., Basham, M., & French, A. P.
- Affiliations
- University of Nottingham (GB)
- Diamond Light Source (GB)
- National University of Singapore (SG)
- Rutgers, The State University of New Jersey (US)
- Baylor College of Medicine (US)
- Corresponding author
- Basham, Mark
- ORCID
- Venue
- Journal of Structural Biology
- Publisher
- Elsevier BV
- Published
- 2017-04
- Volume
- 198
- Issue
- 1
- Pages
- 43-53
- ISSN
- 1047-8477 (ISSN-L)
- PMC
- PMC5405849
- OpenAlex
- W2592107721
- Cited by
- 89
- References
- 54
- Citations by year
- 2025: 4 · 2024: 10 · 2023: 13 · 2022: 8 · 2021: 15 · 2020: 9 · 2019: 11 · 2018: 10 · 2017: 7 · 2015: 1 · 2012: 1
- FWCI
- 5.94
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Structural Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Nottingham ePrints (University of Nottingham) (submittedVersion, cc-by)
- Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
- Funders
- National Institutes of Health (P41GM103832, P01NS092525)
- Diamond Light Source
- National University of Singapore
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Advanced Electron Microscopy Techniques and Applications
- Keywords
- volume segmentation, hierarchical segmentation, interactive segmentation, user annotation, limited training data, low-dose electron microscopy, noisy images
- MeSH terms
- Machine Learning, Algorithms, Software, Datasets as Topic, Data Curation
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