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

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 single-cell 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.