Deep learning strategies for scalable analysis of high-resolution brain imagery

Giacomo Mazzamuto; Francesco Orsini; Matteo Roffilli; Paolo Frasconi; Francesco S. Pavone; Ludovico Silvestri

doi:10.1117/12.2527055

22 July 2019 Deep learning strategies for scalable analysis of high-resolution brain imagery

Giacomo Mazzamuto, Francesco Orsini, Matteo Roffilli, Paolo Frasconi, Francesco S. Pavone, Ludovico Silvestri

Proceedings Volume 11076, Advances in Microscopic Imaging II; 110760C (2019) https://doi.org/10.1117/12.2527055
Event: European Conferences on Biomedical Optics, 2019, Munich, Germany

Abstract

Deciphering brain architecture at a system level requires the ability to quantitatively map its structure with cellular and subcellular resolution. Besides posing significant challenges to current optical microscopy methods, this ambitious goal requires the development of a new generation of tools to make sense of the huge number of raw images generated, which can easily exceed several TeraBytes for a single sample. We present an integrated pipeline enabling transformation of the acquired dataset from a collection of voxel gray levels to a semantic representation of the sample. This pipeline starts with a software for image stitching that computes global optimal alignment of the 3D tiles. The fused volume is then accessed virtually by means of a dedicated API (Application Programming Interface). The virtually fused volume is then processed to extract meaningful information. We demonstrate two complementary approaches based on deep convolutional networks. In one case, a 3D conv-net is used to ‘semantically deconvolve’ the image, allowing accurate localization of neuronal bodies with standard clustering algorithms (e.g. mean shift). The scalability of this approach is demonstrated by mapping the spatial distribution of different neuronal populations in a whole mouse brain with singlecell resolution. To go beyond simple localization, we exploited a 2D conv-net estimating for each pixel the probability of being part of a neuron. The output of the net is then processed with a contour finding algorithm, obtaining reliable segmentation of cell morphology. This information can be used to classify neurons, expanding the potential of chemical labeling strategies.

Citation Download Citation

Giacomo Mazzamuto, Francesco Orsini, Matteo Roffilli, Paolo Frasconi, Francesco S. Pavone, and Ludovico Silvestri "Deep learning strategies for scalable analysis of high-resolution brain imagery", Proc. SPIE 11076, Advances in Microscopic Imaging II, 110760C (22 July 2019); https://doi.org/10.1117/12.2527055

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
3 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Brain

Image segmentation

Neuroimaging

Neurons

Brain mapping

Microscopes

3D image processing

Show All Keywords

Keywords/Phrases

Search In:

Publication Years