Selective labeling: identifying representative sub-volumes for interactive segmentation

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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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Bibliographic Details
Main Authors:	Luengo, Imanol, Basham, Mark, French, Andrew P.
Format:	Article
Published:	Springer Verlag 2016
Subjects:	Unsupervised; Sub-volume proposals; Interactive segmentation; Active learning; Affinity clustering; Supervoxels
Online Access:	https://eprints.nottingham.ac.uk/44843/

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