Machine learning applied to 3D brain images

We developed softwares based on deep learning strategies for cell-counting and shape recognition in 3D brain images reconstructed from advanced ligh microscopy acquisitions, both on mouse and human samples.

Machine learning applied to 3D brain images /images/research-lines/Purkinje.png

People Involved

Ludovico Silvestri, Giacomo Mazzamuto, Leonardo Sacconi, Francesco Saverio Pavone

External Collaborators

Giulio Iannello, Campus Bio-Medico University, Roma
Paolo Frasconi, Department of Information Engineering - University of Florence

Grants

HBP-SGA1

Selected Recent Publications

Silvestri L., Paciscopi M., Soda P., Biamonte F., Iannello G., Frasconi P., Pavone F. S., “Quantitative neuroanatomy of all Purkinje cells with light sheet microscopy and high-throughput image analysis.” Front Neuroanat 9: 68, 2015

Soda P., Acciai L., Cordelli E., Costantini I., Sacconi L., Pavone F. S., Conti V., Guerrini R., Frasconi P., Iannello G., “Computer-based automatic identification of neurons in gigavoxel-sized 3D human brain images.” Conf Proc IEEE Eng Med Biol Soc : 7724-7727, 2015

M. Asllani, J. D. Challenger, F. S. Pavone, L. Sacconi, D. Fanelli, The theory of pattern formation on directed networks, Nature Communications, Vol. 5, p. 4517, DOI: 10.1038/ncomms5517, 2014, July 31.

Frasconi P., Silvestri L., Soda P., Cortini R., Pavone F. S., Iannello G., “Large-scale automated identification of mouse brain cells in confocal light sheet microscopy images.” Bioinformatics 30(17): i587-593, 2014