Fast Optimal Transport Averaging of Neuroimaging Data

Knowing how the Human brain is anatomically and functionally organized at the level of a group of healthy individuals or patients is the primary goal of neuroimaging research. Yet computing an average of brain imaging data defined over a voxel grid or a triangulation remains a challenge. Data are large, the geometry of the brain is complex and the between subjects variability leads to spatially or temporally non-overlapping effects of interest. To address the problem of variability, data are commonly smoothed before group linear averaging. In this work we build on ideas originally introduced by Kantorovich to propose a new algorithm that can average efficiently non-normalized data defined over arbitrary discrete domains using transportation metrics. We show how Kantorovich means can be linked to Wasserstein barycenters in order to take advantage of an entropic smoothing approach. It leads to a smooth convex optimization problem and an algorithm with strong convergence guarantees. We illustrate the versatility of this tool and its empirical behavior on functional neuroimaging data, functional MRI and magnetoencephalography (MEG) source estimates, defined on voxel grids and triangulations of the folded cortical surface.Comment: Information Processing in Medical Imaging (IPMI), Jun 2015, Isle of Skye, United Kingdom. Springer, 201

The structural and chemical basis of temporary adhesion in the sea star Asterina gibbosa

Background: Marine biological adhesives are a promising source of inspiration for biomedical and industrial applications. Nevertheless, natural adhesives and especially temporary adhesion systems are mostly unexplored. Sea stars are able to repeatedly attach and detach their hydraulic tube feet. This ability is based on a duo-gland system and, upon detachment, the adhesive material stays behind on the substrate as a 'footprint'. In recent years, characterization of sea star temporary adhesion has been focussed on the forcipulatid species Asterias rubens. Results: We investigated the temporary adhesion system in the distantly related valvatid species Asterina gibbosa. The morphology of tube feet was described using histological sections, transmission-, and scanning electron microscopy. Ultrastructural investigations revealed two adhesive gland cell types that both form electron-dense secretory granules with a more lucid outer rim and one de-adhesive gland cell type with homogenous granules. The footprints comprised a meshwork on top of a thin layer. This topography was consistently observed using various methods like scanning electron microscopy, 3D confocal interference microscopy, atomic force microscopy, and light microscopy with crystal violet staining. Additionally, we tested 24 commercially available lectins and two antibodies for their ability to label the adhesive epidermis and footprints. Out of 15 lectins labelling structures in the area of the duo-gland adhesive system, only one also labelled footprints indicating the presence of glycoconjugates with α-linked mannose in the secreted material. Conclusion: Despite the distant relationship between the two sea star species, the morphology of tube feet and topography of footprints in A. gibbosa shared many features with the previously described findings in A. rubens. These similarities might be due to the adaptation to a benthic life on rocky intertidal areas. Lectin- and immuno-labelling indicated similarities but also some differences in adhesive composition between the two species. Further research on the temporary adhesive of A. gibbosa will allow the identification of conserved motifs in sea star adhesion and might facilitate the development of biomimetic, reversible glues.</p

MatryODShka: Real-time 6DoF Video View Synthesis using Multi-Sphere Images

We introduce a method to convert stereo 360{\deg} (omnidirectional stereo) imagery into a layered, multi-sphere image representation for six degree-of-freedom (6DoF) rendering. Stereo 360{\deg} imagery can be captured from multi-camera systems for virtual reality (VR), but lacks motion parallax and correct-in-all-directions disparity cues. Together, these can quickly lead to VR sickness when viewing content. One solution is to try and generate a format suitable for 6DoF rendering, such as by estimating depth. However, this raises questions as to how to handle disoccluded regions in dynamic scenes. Our approach is to simultaneously learn depth and disocclusions via a multi-sphere image representation, which can be rendered with correct 6DoF disparity and motion parallax in VR. This significantly improves comfort for the viewer, and can be inferred and rendered in real time on modern GPU hardware. Together, these move towards making VR video a more comfortable immersive medium.Comment: 25 pages, 13 figures, Published at European Conference on Computer Vision (ECCV 2020), Project Page: http://visual.cs.brown.edu/matryodshk

Perceptual quality of BRDF approximations: dataset and metrics

International audienceBidirectional Reflectance Distribution Functions (BRDFs) are pivotal to the perceived realism in image synthesis. While measured BRDF datasets are available, reflectance functions are most of the time approximated by analytical formulas for storage efficiency reasons. These approximations are often obtained by minimizing metrics such as L 2 —or weighted quadratic—distances, but these metrics do not usually correlate well with perceptual quality when the BRDF is used in a rendering context, which motivates a perceptual study. The contributions of this paper are threefold. First, we perform a large-scale user study to assess the perceptual quality of 2026 BRDF approximations, resulting in 84138 judgments across 1005 unique participants. We explore this dataset and analyze perceptual scores based on material type and illumination. Second, we assess nine analytical BRDF models in their ability to approximate tabulated BRDFs. Third, we assess several image-based and BRDF-based (Lp, optimal transport and kernel distance) metrics in their ability to approximate perceptual similarity judgments

Primal Heuristics for Wasserstein Barycenters

This paper presents primal heuristics for the computation of Wasserstein Barycenters of a given set of discrete probability measures. The computation of a Wasserstein Barycenter is formulated as an optimization problem over the space of discrete probability measures. In practice, the barycenter is a discrete probability measure which minimizes the sum of the pairwise Wasserstein distances between the barycenter itself and each input measure. While this problem can be formulated using Linear Programming techniques, it remains a challenging problem due to the size of real-life instances. In this paper, we propose simple but efficient primal heuristics, which exploit the properties of the optimal plan obtained while computing the Wasserstein Distance between a pair of probability measures. In order to evaluate the proposed primal heuristics, we have performed extensive computational tests using random Gaussian distributions, the MNIST handwritten digit dataset, and the Fashion MNIST dataset introduced by Zalando. We also used Translated MNIST, a modification of MNIST which contains original images, rescaled randomly and translated into a larger image. We compare the barycenters computed by our heuristics with the exact solutions obtained with a commercial Linear Programming solver, and with a state-of-the-art algorithm based on Gaussian convolutions. Our results show that the proposed heuristics yield in very short run time and an average optimality gap significantly smaller than 1%

Mechanical adaptability of sea cucumber Cuvierian tubules involves a mutable collagenous tissue

Despite their soft body and slow motion, sea cucumbers present a low predation rate, reflecting the presence of efficient defence systems. For instance, members of the family Holothuriidae rely on Cuvierian tubules for their defence. These tubules are normally stored in the posterior coelomic cavity of the animal, but when the sea cucumber is threatened by a potential predator, they are expelled through the cloacal aperture, elongate, become sticky and entangle and immobilise the predator in a matter of seconds. The mechanical properties (extensibility, tensile strength, stiffness and toughness) of quiescent (i.e. in the body cavity) and elongated (i.e. after expulsion) Cuvierian tubules were investigated in the species Holothuria forskali using traction tests. Important mechanical differences were measured between the two types of tubules, reflecting adaptability to their operating mode: to ease elongation, quiescent tubules present a low resistance to extension, while elongated tubules present a high toughness to resist tractions generated by the predator. We demonstrate that a mutable collagenous tissue (MCT) is involved in the functioning of these organs: (1) some mechanical properties of Cuvierian tubules are modified by incubation in a cell-disrupting solution; (2) the connective tissue layer encloses juxtaligamental-like cells, a cell type present in all MCTs; and (3) tensilin, a MCT stiffening protein, was localised inside these cells. Cuvierian tubules thus appear to enclose a new type of MCT which shows irreversible stiffening