Selective labeling: identifying representative sub-volumes for interactive segmentation
Automatic segmentation of challenging biomedical volumes with multiple objects is still an open research field. Automatic approaches usually require a large amount of training data to be able to model the complex and often noisy appearance and structure of biological organelles and their boundaries....
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| Format: | Article |
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Springer Verlag
2016
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| Online Access: | https://eprints.nottingham.ac.uk/44843/ |
| _version_ | 1848797010952454144 |
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| author | Luengo, Imanol Basham, Mark French, Andrew P. |
| author_facet | Luengo, Imanol Basham, Mark French, Andrew P. |
| author_sort | Luengo, Imanol |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Automatic segmentation of challenging biomedical volumes with multiple objects is still an open research field. Automatic approaches usually require a large amount of training data to be able to model the complex and often noisy appearance and structure of biological organelles and their boundaries. However, due to the variety of different biological specimens and the large volume sizes of the datasets, training data is costly to produce, error prone and sparsely available. Here, we propose a novel Selective Labeling algorithm to overcome these challenges; an unsupervised sub-volume proposal method that identifies the most representative regions of a volume. This massively-reduced subset of regions are then manually labeled and combined with an active learning procedure to fully segment the volume. Results on a publicly available EM dataset demonstrate the quality of our approach by achieving equivalent segmentation accuracy with only 5 % of the training data. |
| first_indexed | 2025-11-14T19:57:05Z |
| format | Article |
| id | nottingham-44843 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:57:05Z |
| publishDate | 2016 |
| publisher | Springer Verlag |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-448432020-05-04T18:10:16Z https://eprints.nottingham.ac.uk/44843/ Selective labeling: identifying representative sub-volumes for interactive segmentation Luengo, Imanol Basham, Mark French, Andrew P. Automatic segmentation of challenging biomedical volumes with multiple objects is still an open research field. Automatic approaches usually require a large amount of training data to be able to model the complex and often noisy appearance and structure of biological organelles and their boundaries. However, due to the variety of different biological specimens and the large volume sizes of the datasets, training data is costly to produce, error prone and sparsely available. Here, we propose a novel Selective Labeling algorithm to overcome these challenges; an unsupervised sub-volume proposal method that identifies the most representative regions of a volume. This massively-reduced subset of regions are then manually labeled and combined with an active learning procedure to fully segment the volume. Results on a publicly available EM dataset demonstrate the quality of our approach by achieving equivalent segmentation accuracy with only 5 % of the training data. Springer Verlag 2016-09-21 Article PeerReviewed Luengo, Imanol, Basham, Mark and French, Andrew P. (2016) Selective labeling: identifying representative sub-volumes for interactive segmentation. Lecture Notes in Computer Science, 9993 . pp. 17-24. ISSN 0302-9743 Unsupervised; Sub-volume proposals; Interactive segmentation; Active learning; Affinity clustering; Supervoxels https://link.springer.com/chapter/10.1007/978-3-319-47118-1_3 doi:10.1007/978-3-319-47118-1_3 doi:10.1007/978-3-319-47118-1_3 |
| spellingShingle | Unsupervised; Sub-volume proposals; Interactive segmentation; Active learning; Affinity clustering; Supervoxels Luengo, Imanol Basham, Mark French, Andrew P. Selective labeling: identifying representative sub-volumes for interactive segmentation |
| title | Selective labeling: identifying representative sub-volumes for interactive segmentation |
| title_full | Selective labeling: identifying representative sub-volumes for interactive segmentation |
| title_fullStr | Selective labeling: identifying representative sub-volumes for interactive segmentation |
| title_full_unstemmed | Selective labeling: identifying representative sub-volumes for interactive segmentation |
| title_short | Selective labeling: identifying representative sub-volumes for interactive segmentation |
| title_sort | selective labeling: identifying representative sub-volumes for interactive segmentation |
| topic | Unsupervised; Sub-volume proposals; Interactive segmentation; Active learning; Affinity clustering; Supervoxels |
| url | https://eprints.nottingham.ac.uk/44843/ https://eprints.nottingham.ac.uk/44843/ https://eprints.nottingham.ac.uk/44843/ |