Paper Info
Title
Feasibility of 3D Reconstruction of Neural Morphology Using Expansion Microscopy and Barcode-Guided Agglomeration.
Abstract
We here introduce and study the properties, via computer simulation, of a candidate automated approach to algorithmic reconstruction of dense neural morphology, based on simulated data of the kind that would be obtained via two emerging molecular technologies-expansion microscopy (ExM) and in-situ molecular barcoding. We utilize a convolutional neural network to detect neuronal boundaries from protein-tagged plasma membrane images obtained via ExM, as well as a subsequent supervoxel-merging pipeline guided by optical readout of information-rich, cell-specific nucleic acid barcodes. We attempt to use conservative imaging and labeling parameters, with the goal of establishing a baseline case that points to the potential feasibility of optical circuit reconstruction, leaving open the possibility of higher-performance labeling technologies and algorithms. We find that, even with these conservative assumptions, an all-optical approach to dense neural morphology reconstruction may be possible via the proposed algorithmic framework. Future work should explore both the design-space of chemical labels and barcodes, aswell as algorithms, to ultimately enable routine, high-performance optical circuit reconstruction.
Year
DOI
Venue
2017
10.3389/fncom.2017.00097
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
Keywords
Field
DocType
neural morphology,3-D reconstruction,expansion microscopy,RNA barcode,convolutional neural network,agglomeration
Computer vision,Economies of agglomeration,Computer science,Convolutional neural network,Artificial intelligence,Microscopy,Barcode,Machine learning,3D reconstruction
Journal
Volume
ISSN
Citations 
11
1662-5188
0
PageRank 
References 
Authors
0.34
3
6
Name
Order
Citations
PageRank
Young-Gyu Yoon11019.29
Peilun Dai200.34
Jeremy Wohlwend301.69
Jae-Byum Chang420.74
Adam Henry Marblestone5374.29
E. Boyden, III6316.37