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Summary: Model-based autosegmentation of brain structures in the honeybee using
statistical shape models
K. Neubert1
, H. Lamecker2
, H.-C. Hege2
, R. Menzel1
, J. Rybak1
1
Institute of Neurobiology, Free University of Berlin, 2
Zuse Institute, Berlin
Surface-based brain atlases like the Honeybee Brain Atlas (www.neurobiologie.fu-berlin.de
/beebrain/),(Brandt et al.,2005)1
compare neuronal morphologies in a 3D context, and serve as
databases and communicative platforms. Transforming neurons into the atlas requires
manually segmenting neuropils in confocal images, a time-consuming task requiring expertise
in identifying biological structures which can result in different outcomes from various
segmenters.
Statistical shape models are promising approaches for automatic segmentation of 3D medical
data (CT)2
. A deformable linear 3D neuropil model is generated by principle component
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