SuRVoS: Super-Region Volume Segmentation workbench
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 ch...
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Published: |
Elsevier
2017
|
| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/41190/ |
| _version_ | 1848796217662767104 |
|---|---|
| author | 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. |
| author_facet | 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. |
| author_sort | Luengo, Imanol |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:44:29Z |
| format | Article |
| id | nottingham-41190 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:44:29Z |
| publishDate | 2017 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-411902020-05-04T18:33:27Z https://eprints.nottingham.ac.uk/41190/ 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. 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. Elsevier 2017-02-27 Article PeerReviewed 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 and French, Andrew P. (2017) SuRVoS: Super-Region Volume Segmentation workbench. Journal of Structural Biology . ISSN 1095-8657 (In Press) Interactive segmentation; Hierarchical segmentation; Super-Regions; Semi-supervised learning; Cryo soft X-ray tomography; Cryo electron tomography https://doi.org/10.1016/j.jsb.2017.02.007 doi:10.1016/j.jsb.2017.02.007 doi:10.1016/j.jsb.2017.02.007 |
| spellingShingle | Interactive segmentation; Hierarchical segmentation; Super-Regions; Semi-supervised learning; Cryo soft X-ray tomography; Cryo electron tomography 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. SuRVoS: Super-Region Volume Segmentation workbench |
| title | SuRVoS: Super-Region Volume Segmentation workbench |
| title_full | SuRVoS: Super-Region Volume Segmentation workbench |
| title_fullStr | SuRVoS: Super-Region Volume Segmentation workbench |
| title_full_unstemmed | SuRVoS: Super-Region Volume Segmentation workbench |
| title_short | SuRVoS: Super-Region Volume Segmentation workbench |
| title_sort | survos: super-region volume segmentation workbench |
| topic | Interactive segmentation; Hierarchical segmentation; Super-Regions; Semi-supervised learning; Cryo soft X-ray tomography; Cryo electron tomography |
| url | https://eprints.nottingham.ac.uk/41190/ https://eprints.nottingham.ac.uk/41190/ https://eprints.nottingham.ac.uk/41190/ |