Image-guided ToF depth upsampling: a survey

Recently, there has been remarkable growth of interest in the development and applications of time-of-flight (ToF) depth cameras. Despite the permanent improvement of their characteristics, the practical applicability of ToF cameras is still limited by low resolution and quality of depth measurements. This has motivated many researchers to combine ToF cameras with other sensors in order to enhance and upsample depth images. In this paper, we review the approaches that couple ToF depth images with high-resolution optical images. Other classes of upsampling methods are also briefly discussed. Finally, we provide an overview of performance evaluation tests presented in the related studies

Search for dark photons in rare Z boson decays with the ATLAS detector

A search for events with a dark photon produced in association with a dark Higgs boson via rare decays of the standard model Z boson is presented, using 139     fb − 1 of √ s = 13     TeV proton-proton collision data recorded by the ATLAS detector at the Large Hadron Collider. The dark boson decays into a pair of dark photons, and at least two of the three dark photons must each decay into a pair of electrons or muons, resulting in at least two same-flavor opposite-charge lepton pairs in the final state. The data are found to be consistent with the background prediction, and upper limits are set on the dark photon’s coupling to the dark Higgs boson times the kinetic mixing between the standard model photon and the dark photon, α D ϵ 2 , in the dark photon mass range of [5, 40] GeV except for the Υ mass window [8.8, 11.1] GeV. This search explores new parameter space not previously excluded by other experiments

Combined measurement of the Higgs boson mass from the H → γγ and H → ZZ∗ → 4ℓ decay channels with the ATLAS detector using √s = 7, 8, and 13 TeV pp collision data

A measurement of the mass of the Higgs boson combining the H → Z Z ∗ → 4 ℓ and H → γ γ decay channels is presented. The result is based on 140     fb − 1 of proton-proton collision data collected by the ATLAS detector during LHC run 2 at a center-of-mass energy of 13 TeV combined with the run 1 ATLAS mass measurement, performed at center-of-mass energies of 7 and 8 TeV, yielding a Higgs boson mass of 125.11 ± 0.09 ( stat ) ± 0.06 ( syst ) = 125.11 ± 0.11     GeV . This corresponds to a 0.09% precision achieved on this fundamental parameter of the Standard Model of particle physics

Automatic Colon Segmentation with Dual Scan CT Colonography

We present a fully automated three-dimensional (3-D) segmentation algorithm to extract the colon lumen surface in CT colonography. Focusing on significant-size polyp detection, we target at an efficient algorithm that maximizes overall colon coverage, minimizes the extracolonic components, maintains local shape accuracy, and achieves high segmentation speed. Two-dimensional (2-D) image processing techniques are employed first, resulting in automatic seed placement and better colon coverage. This is followed by near-air threshold 3-D region-growing using an improved marching-cubes algorithm, which provides fast and accurate surface generation. The algorithm constructs a well-organized vertex-triangle structure that uniquely employs a hash table method, yielding an order of magnitude speed improvement. We segment two scans, prone and supine, independently and with the goal of improved colon coverage. Both segmentations would be available for subsequent polyp detection systems. Segmenting and analyzing both scans improves surface coverage by at least 6% over supine or prone alone. According to subjective evaluation, the average coverage is about 87.5% of the entire colon. Employing near-air threshold and elongation criteria, only 6% of the data sets include extracolonic components (EC) in the segmentation. The observed surface shape accuracy of the segmentation is adequate for significant-size (6 mm) polyp detection, which is also verified by the results of the prototype detection algorithm. The segmentation takes less than 5 minutes on an AMD 1-GHz single-processor PC, which includes reading the volume data and writing the surface results. The surface-based segmentation algorithm is practical for subsequent polyp detection algorithms in that it produces high coverage, has a low EC rate, maintains local shape accuracy, and has a computational efficiency that makes real-time polyp detection possible. A fully automatic or computer-aided polyp detection system using this technique is likely to benefit future colon cancer early screening

Content-Based Image Retrieval in Radiology: Current Status and Future Directions

Diagnostic radiology requires accurate interpretation of complex signals in medical images. Content-based image retrieval (CBIR) techniques could be valuable to radiologists in assessing medical images by identifying similar images in large archives that could assist with decision support. Many advances have occurred in CBIR, and a variety of systems have appeared in nonmedical domains; however, permeation of these methods into radiology has been limited. Our goal in this review is to survey CBIR methods and systems from the perspective of application to radiology and to identify approaches developed in nonmedical applications that could be translated to radiology. Radiology images pose specific challenges compared with images in the consumer domain; they contain varied, rich, and often subtle features that need to be recognized in assessing image similarity. Radiology images also provide rich opportunities for CBIR: rich metadata about image semantics are provided by radiologists, and this information is not yet being used to its fullest advantage in CBIR systems. By integrating pixel-based and metadata-based image feature analysis, substantial advances of CBIR in medicine could ensue, with CBIR systems becoming an important tool in radiology practice