DocumentCode
617606
Title
Semi-automatic neuron segmentation in electron microscopy images via sparse labeling
Author
Jones, Clayton ; Ting Liu ; Ellisman, Mark ; Tasdizen, Tolga
Author_Institution
Sci. Comput. & Imaging Inst., Univ. of Utah, Salt Lake City, UT, USA
fYear
2013
fDate
7-11 April 2013
Firstpage
1304
Lastpage
1307
Abstract
We introduce a novel method for utilizing user input to sparsely label membranes in electron microscopy images. Using gridlines as guides, the user marks where the guides cross the membrane to generate a sparsely labeled image. We use a best path algorithm to connect each of the sparse membrane labels. The resulting segmentation has a significantly better Rand error than automatic methods while requiring as little as 2% of the image to be labeled.
Keywords
biological techniques; biology computing; biomembranes; compressed sensing; electron microscopy; image segmentation; neurophysiology; Rand error; automatic method; best path algorithm; electron microscopy image; gridline; semiautomatic neuron segmentation; sparse labeling; sparsely label membrane; user input; Biomembranes; Electron microscopy; Image segmentation; Labeling; Merging; Mice; biological segmentation; connectomics; electron microscopy; semi-automatic segmentation;
fLanguage
English
Publisher
ieee
Conference_Titel
Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on
Conference_Location
San Francisco, CA
ISSN
1945-7928
Print_ISBN
978-1-4673-6456-0
Type
conf
DOI
10.1109/ISBI.2013.6556771
Filename
6556771
Link To Document