Large-scale automated identification of mouse brain cells in confocal light sheet microscopy images

Year: 2014

Authors: Frasconi P., Silvestri L., Soda P., Cortini R., Pavone F. S., Iannello G.

Autors Affiliation: Univ Florence, Dept Informat Engn DINFO, I-50139 Florence, Italy; Univ Florence, LENS, I-50019 Sesto Fiorentino, Italy; Univ Campus Biomed Roma, Integrated Res Ctr, I-00128 Rome, Italy

Abstract: Motivation: Recently, confocal light sheet microscopy has enabled high-throughput acquisition of whole mouse brain 3D images at the micron scale resolution. This poses the unprecedented challenge of creating accurate digital maps of the whole set of cells in a brain.

Results: We introduce a fast and scalable algorithm for fully automated cell identification. We obtained the whole digital map of Purkinje cells in mouse cerebellum consisting of a set of 3D cell center coordinates. The method is accurate and we estimated an F1 measure of 0.96 using 56 representative volumes, totaling 1.09 GVoxel and containing 4138 manually annotated soma centers.

Availability and implementation: Source code and its documentation are available at The whole pipeline of methods is implemented in Python and makes use of Pylearn2 and modified parts of Scikit-learn. Brain images are available on request.


Volume: 30 (17)      Pages from: i587  to: i593

More Information: The work of L.S. and F.P. was supported by E.U. grants FP7 228334, FP7 284464 and FET flagship HBP (604102).
DOI: 10.1093/bioinformatics/btu469

Citations: 45
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