Label-driven weakly-supervised learning for multimodal deformable image registration

Spatially aligning medical images from different modalities remains a challenging task, especially for intraoperative applications that require fast and robust algorithms. We propose a weakly-supervised, label-driven formulation for learning 3D voxel correspondence from higher-level label correspondence, thereby bypassing classical intensity-based image similarity measures. During training, a convolutional neural network is optimised by outputting a dense displacement field (DDF) that warps a set of available anatomical labels from the moving image to match their corresponding counterparts in the fixed image. These label pairs, including solid organs, ducts, vessels, point landmarks and other ad hoc structures, are only required at training time and can be spatially aligned by minimising a cross-entropy function of the warped moving label and the fixed label. During inference, the trained network takes a new image pair to predict an optimal DDF, resulting in a fully-automatic, label-free, real-time and deformable registration. For interventional applications where large global transformation prevails, we also propose a neural network architecture to jointly optimise the global- and local displacements. Experiment results are presented based on cross-validating registrations of 111 pairs of T2-weighted magnetic resonance images and 3D transrectal ultrasound images from prostate cancer patients with a total of over 4000 anatomical labels, yielding a median target registration error of 4.2 mm on landmark centroids and a median Dice of 0.88 on prostate glands.Comment: Accepted to ISBI 201

DeepKey: Towards End-to-End Physical Key Replication From a Single Photograph

This paper describes DeepKey, an end-to-end deep neural architecture capable of taking a digital RGB image of an 'everyday' scene containing a pin tumbler key (e.g. lying on a table or carpet) and fully automatically inferring a printable 3D key model. We report on the key detection performance and describe how candidates can be transformed into physical prints. We show an example opening a real-world lock. Our system is described in detail, providing a breakdown of all components including key detection, pose normalisation, bitting segmentation and 3D model inference. We provide an in-depth evaluation and conclude by reflecting on limitations, applications, potential security risks and societal impact. We contribute the DeepKey Datasets of 5, 300+ images covering a few test keys with bounding boxes, pose and unaligned mask data.Comment: 14 pages, 12 figure

A fast recursive coordinate bisection tree for neighbour search and gravity

We introduce our new binary tree code for neighbour search and gravitational force calculations in an N-particle system. The tree is built in a "top-down" fashion by "recursive coordinate bisection" where on each tree level we split the longest side of a cell through its centre of mass. This procedure continues until the average number of particles in the lowest tree level has dropped below a prescribed value. To calculate the forces on the particles in each lowest-level cell we split the gravitational interaction into a near- and a far-field. Since our main intended applications are SPH simulations, we calculate the near-field by a direct, kernel-smoothed summation, while the far field is evaluated via a Cartesian Taylor expansion up to quadrupole order. Instead of applying the far-field approach for each particle separately, we use another Taylor expansion around the centre of mass of each lowest-level cell to determine the forces at the particle positions. Due to this "cell-cell interaction" the code performance is close to O(N) where N is the number of used particles. We describe in detail various technicalities that ensure a low memory footprint and an efficient cache use. In a set of benchmark tests we scrutinize our new tree and compare it to the "Press tree" that we have previously made ample use of. At a slightly higher force accuracy than the Press tree, our tree turns out to be substantially faster and increasingly more so for larger particle numbers. For four million particles our tree build is faster by a factor of 25 and the time for neighbour search and gravity is reduced by more than a factor of 6. In single processor tests with up to 10^8 particles we confirm experimentally that the scaling behaviour is close to O(N). The current Fortran 90 code version is OpenMP-parallel and scales excellently with the processor number (=24) of our test machine.Comment: 12 pages, 16 figures, 1 table, accepted for publication in MNRAS on July 28, 201

Sugawara-type constraints in hyperbolic coset models

In the conjectured correspondence between supergravity and geodesic models on infinite-dimensional hyperbolic coset spaces, and E10/K(E10) in particular, the constraints play a central role. We present a Sugawara-type construction in terms of the E10 Noether charges that extends these constraints infinitely into the hyperbolic algebra, in contrast to the truncated expressions obtained in arXiv:0709.2691 that involved only finitely many generators. Our extended constraints are associated to an infinite set of roots which are all imaginary, and in fact fill the closed past light-cone of the Lorentzian root lattice. The construction makes crucial use of the E10 Weyl group and of the fact that the E10 model contains both D=11 supergravity and D=10 IIB supergravity. Our extended constraints appear to unite in a remarkable manner the different canonical constraints of these two theories. This construction may also shed new light on the issue of `open constraint algebras' in traditional canonical approaches to gravity.Comment: 49 page