Visualization of mouse barrel cortex using ex-vivo track density imaging

We describe the visualization of the barrel cortex of the primary somatosensory area (S1) of ex vivo adult mouse brain with short-tracks track density imaging (stTDI). stTDI produced much higher definition of barrel structures than conventional fractional anisotropy (FA), directionally-encoded color FA maps, spin-echo and T2-weighted imaging and gradient echo Ti/T2*-weighted imaging. 3D high angular resolution diffusion imaging (HARDI) data were acquired at 48 micron isotropic resolution for a (3 mm)3 block of cortex containing the barrel field and reconstructed using stTDI at 10 micron isotropic resolution. HARDI data were also acquired at 100 micron isotropic resolution to image the whole brain and reconstructed using stTDI at 20 micron isotropic resolution. The 10 micron resolution stTDI maps showed exceptionally clear delineation of barrel structures. Individual barrels could also be distinguished in the 20 micron stTDI maps but the septa separating the individual barrels appeared thicker compared to the 10 micron maps, indicating that the ability of stTDI to produce high quality structural delineation is dependent upon acquisition resolution. Close homology was observed between the barrel structure delineated using stTDI and reconstructed histological data from the same samples. stTDI also detects barrel deletions in the posterior medial barrel sub-field in mice with infraorbital nerve cuts. The results demonstrate that stTDI is a novel imaging technique that enables three-dimensional characterization of complex structures such as the barrels in S1 and provides an important complementary non-invasive imaging tool for studying synaptic connectivity, development and plasticity of the sensory system. (C) 2013 Elsevier Inc. All rights reserved

Generalization Properties of Geometric 3D Deep Learning Models for Medical Segmentation

Recent advances in medical Deep Learning (DL) have enabled the significant reduction in time required to extract anatomical segmentations from 3-Dimensional images in an unprecedented manner. Among these methods, supervised segmentation-based approaches using variations of the UNet architecture remain extremely popular. However, these methods remain tied to the input images' resolution, and their generalisation performance relies heavily on the data distribution over the training dataset. Recently, a new family of approaches based on 3D geometric DL has emerged. These approaches encompass both implicit and explicit surface representation methods and promises to represent a 3D volume using a continuous representation of its surface whilst conserving its topological properties. It has been conjectured that these geometrical methods are more robust to out-of-distribution data and have increased generalisation properties. In this paper, we test these hypotheses for the challenging task of cortical surface reconstruction (CSR) using recently proposed architectures.</p