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DP2: Distributed 3D image segmentation using micro-labor workforce

Summary: This application note describes a new scalable semi-automatic approach, the Dual Point Decision Process, for segmentation of 3D structures contained in 3D microscopy. The segmentation problem is distributed to many individual workers such that each receives only simple questions regarding w...

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Detalles Bibliográficos
Autores principales: Giuly, Richard J., Kim, Keun-Young, Ellisman, Mark H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3654713/
https://www.ncbi.nlm.nih.gov/pubmed/23574738
http://dx.doi.org/10.1093/bioinformatics/btt154
Descripción
Sumario:Summary: This application note describes a new scalable semi-automatic approach, the Dual Point Decision Process, for segmentation of 3D structures contained in 3D microscopy. The segmentation problem is distributed to many individual workers such that each receives only simple questions regarding whether two points in an image are placed on the same object. A large pool of micro-labor workers available through Amazon’s Mechanical Turk system provides the labor in a scalable manner. Availability and implementation: Python-based code for non-commercial use and test data are available in the source archive at https://sites.google.com/site/imagecrowdseg/. Contact: rgiuly@ucsd.edu Supplementary information: Supplementary data are available at Bioinformatics online.