Application of Neural Networks for Energy Reconstruction

The possibility to use Neural Networks for reconstruction of the energy deposited in the calorimetry system of the CMS detector is investigated. It is shown that using feed - forward neural network, good linearity, Gaussian energy distribution and good energy resolution can be achieved. Significant improvement of the energy resolution and linearity is reached in comparison with other weighting methods for energy reconstruction.Comment: 18 pages, 13 figures, LATEX, submitted to: Nuclear Instruments & Methods

EchoFusion: Tracking and Reconstruction of Objects in 4D Freehand Ultrasound Imaging without External Trackers

Ultrasound (US) is the most widely used fetal imaging technique. However, US images have limited capture range, and suffer from view dependent artefacts such as acoustic shadows. Compounding of overlapping 3D US acquisitions into a high-resolution volume can extend the field of view and remove image artefacts, which is useful for retrospective analysis including population based studies. However, such volume reconstructions require information about relative transformations between probe positions from which the individual volumes were acquired. In prenatal US scans, the fetus can move independently from the mother, making external trackers such as electromagnetic or optical tracking unable to track the motion between probe position and the moving fetus. We provide a novel methodology for image-based tracking and volume reconstruction by combining recent advances in deep learning and simultaneous localisation and mapping (SLAM). Tracking semantics are established through the use of a Residual 3D U-Net and the output is fed to the SLAM algorithm. As a proof of concept, experiments are conducted on US volumes taken from a whole body fetal phantom, and from the heads of real fetuses. For the fetal head segmentation, we also introduce a novel weak annotation approach to minimise the required manual effort for ground truth annotation. We evaluate our method qualitatively, and quantitatively with respect to tissue discrimination accuracy and tracking robustness.Comment: MICCAI Workshop on Perinatal, Preterm and Paediatric Image analysis (PIPPI), 201

Unsupervised Odometry and Depth Learning for Endoscopic Capsule Robots

In the last decade, many medical companies and research groups have tried to convert passive capsule endoscopes as an emerging and minimally invasive diagnostic technology into actively steerable endoscopic capsule robots which will provide more intuitive disease detection, targeted drug delivery and biopsy-like operations in the gastrointestinal(GI) tract. In this study, we introduce a fully unsupervised, real-time odometry and depth learner for monocular endoscopic capsule robots. We establish the supervision by warping view sequences and assigning the re-projection minimization to the loss function, which we adopt in multi-view pose estimation and single-view depth estimation network. Detailed quantitative and qualitative analyses of the proposed framework performed on non-rigidly deformable ex-vivo porcine stomach datasets proves the effectiveness of the method in terms of motion estimation and depth recovery.Comment: submitted to IROS 201

Advanced hybrid tracking through neural network regression

International audienceWe present an optical-haptic tracking system suitable for augmented and virtual reality applications. The paper addresses tow issues of such system. The first one concerns the calibration method that can be used to calibrate the force feedback device: SPIDAR. The second contribution is about the development of a hybrid tracking system. The proposed hybridization aims to provide both accurate and interrupted position data

Magnetic-Visual Sensor Fusion-based Dense 3D Reconstruction and Localization for Endoscopic Capsule Robots

Reliable and real-time 3D reconstruction and localization functionality is a crucial prerequisite for the navigation of actively controlled capsule endoscopic robots as an emerging, minimally invasive diagnostic and therapeutic technology for use in the gastrointestinal (GI) tract. In this study, we propose a fully dense, non-rigidly deformable, strictly real-time, intraoperative map fusion approach for actively controlled endoscopic capsule robot applications which combines magnetic and vision-based localization, with non-rigid deformations based frame-to-model map fusion. The performance of the proposed method is demonstrated using four different ex-vivo porcine stomach models. Across different trajectories of varying speed and complexity, and four different endoscopic cameras, the root mean square surface reconstruction errors 1.58 to 2.17 cm.Comment: submitted to IROS 201

Precision measurements of the top quark mass from the Tevatron in the pre-LHC era

The top quark is the heaviest of the six quarks of the Standard Model. Precise knowledge of its mass is important for imposing constraints on a number of physics processes, including interactions of the as yet unobserved Higgs boson. The Higgs boson is the only missing particle of the Standard Model, central to the electroweak symmetry breaking mechanism and generation of particle masses. In this Review, experimental measurements of the top quark mass accomplished at the Tevatron, a proton-antiproton collider located at the Fermi National Accelerator Laboratory, are described. Topologies of top quark events and methods used to separate signal events from background sources are discussed. Data analysis techniques used to extract information about the top mass value are reviewed. The combination of several most precise measurements performed with the two Tevatron particle detectors, CDF and \D0, yields a value of \Mt = 173.2 \pm 0.9 GeV/$c^2$.Comment: This version contains the most up-to-date top quark mass averag