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TECHNIQUES FOR SEMI-AUTOMATED 3D DENDRITIC ARBOR EXTRACTION FROM MEDIUM TO LOW-RESOLUTION AND NOISY FLUORESCENCE MICROSCOPY IMAGES D.B.Ehlenberger1,4; A.Rodriguez1,4; K.T.Kelliher1,4; A.Rocher2,4; S.C.Henderson3,4; P.R.Hof2,4; S.L.Wearne1,2,4* 1. Depts Biomathematical Sci, 2. Neurosci., 3. Mol. Cell. & Developmental Biol., 4. CNIC, Mt Sinai Sch Med, New York, NY, USA Accurate 3D reconstruction and quantification of dendritic branching structure is essential to understanding the structural determinants of neuronal function. We are developing semi-automated techniques that allow interactive user input following automated skeletonization, to correct topologic errors resulting from suboptimal imaging conditions. The robustness of these techniques depends upon the optical resolution, depth of field, pixel size, bit depth and signal-noise ratio of the original images, which can introduce artifactual loops in tree topology and dendritic breakages. To assess minimal image quality required for a given degree of semi-automation, pyramidal neurons from C57BL/6 mice were loaded with Lucifer Yellow and imaged using 3 different imaging systems: confocal and laser scanning, and widefield fluorescence microscopy, over a range of magnifications (10X to 100X), bit depths (8, 12 and 16-bit) and voxel dimensions (i.e. resolution). Images were deconvolved and skeletonized to extract dendritic arbors, which varied in overall size and accuracy depending upon the imaging mode and parameters. We describe semi-automated algorithms for loop resolution and reconnection of broken dendritic segments. New algorithms for defining branch directions and identifying crossover points are introduced. These algorithms are incorporated into a platform-independent GUI, which supports an interactive 3D model editor allowing multiple display modes, including slice viewers and volume rendering, for user-friendly manual editing in 3D. Using these techniques, accurate models of entire digitized neurons can be imported into standard compartmental modeling software for high-resolution dynamical simulation. Support: MH60734, MH58911, DC05699, RR16754, CA095823, HHMI RR. Citation:D.B. Ehlenberger, A. Rodriguez, K.T. Kelliher, A. Rocher, S.C. Henderson, P.R. Hof, S.L. Wearne. TECHNIQUES FOR SEMI-AUTOMATED 3D DENDRITIC ARBOR EXTRACTION FROM MEDIUM TO LOW-RESOLUTION AND NOISY FLUORESCENCE MICROSCOPY IMAGES Program No. 922.27. 2004 Abstract Viewer/Itinerary Planner. Washington, DC: Society for Neuroscience, 2004. Online. 2004 Copyright by the Society for Neuroscience all rights reserved. Permission to republish any abstract or part of any abstract in any form must be obtained in writing from the SfN office prior to publication