Interactive contouring through contextual deep learning

Purpose: To investigate a deep learning approach that enables 3D segmentation of an arbitrary structure of interest given a user provided 2D contour for context. Such an approach could decrease delineation times and improve contouring consistency, particularly for anatomical structures for which no automatic segmentation tools exist. Methods: A series of deep learning segmentation models using a Recurrent Residual U-Net with attention gates was trained with a successively expanding training set. Contextual information was provided to the models, using a previously contoured slice as an input, in addition to the slice to be contoured. In total, 6 models were developed, and 19 different anatomical structures were used for training and testing. Each of the models was evaluated for all 19 structures, even if they were excluded from the training set, in order to assess the model’s ability to segment unseen structures of interest. Each model’s performance was evaluated using the Dice Similarity Coefficient (DSC), Hausdorff distance and relative Added Path Length (APL). Results: The segmentation performance for seen and unseen structures improved when the training set was expanded by addition of structures previously excluded from the training set. A model trained exclusively on heart structures achieved a DSC of 0.33, HD of 44 mm and relative APL of 0.85 when segmenting the spleen, whereas a model trained on a diverse set of structures, but still excluding the spleen, achieved a DSC of 0.80, HD of 13 mm and relative APL of 0.35. Iterative prediction performed better compared to direct prediction when considering unseen structures. Conclusions: Training a contextual deep learning model on a diverse set of structures increases the segmentation performance for the structures in the training set, but importantly enables the model to generalize and make predictions even for unseen structures that were not represented in the training set. This shows that user-provided context can be incorporated into deep learning contouring to facilitate semi-automatic segmentation of CT images for any given structure. Such an approach can enable faster de-novo contouring in clinical practice.</p

Beyond automatic medical image segmentation - the spectrum between fully manual and fully automatic delineation

Semi-automatic and fully automatic contouring tools have emerged as an alternative to fully manual segmentation to reduce time spent contouring and to increase contour quality and consistency. Particularly, fully automatic segmentation has seen exceptional improvements through the use of deep learning in recent years. These fully automatic methods may not require user interactions, but the resulting contours are often not suitable to be used in clinical practice without a review by the clinician. Furthermore, they need large amounts of labeled data to be available for training. This review presents alternatives to manual or fully automatic segmentation methods along the spectrum of variable user interactivity and data availability. The challenge lies to determine how much user interaction is necessary and how this user interaction can be used most effectively. While deep learning is already widely used for fully automatic tools, interactive methods are just at the starting point to be transformed by it. Interaction between clinician and machine, via artificial intelligence, can go both ways and this review will present the avenues that are being pursued to improve medical image segmentation

Charge‐carrier trapping and radiative recombination in metal halide perovskite semiconductors

Trap‐related charge‐carrier recombination fundamentally limits the performance of perovskite solar cells and other optoelectronic devices. While improved fabrication and passivation techniques have reduced trap densities, the properties of trap states and their impact on the charge‐carrier dynamics in metal‐halide perovskites are still under debate. Here, a unified model is presented of the radiative and nonradiative recombination channels in a mixed formamidinium‐cesium lead iodide perovskite, including charge‐carrier trapping, de‐trapping and accumulation, as well as higher‐order recombination mechanisms. A fast initial photoluminescence (PL) decay component observed after pulsed photogeneration is demonstrated to result from rapid localization of free charge carriers in unoccupied trap states, which may be followed by de‐trapping, or nonradiative recombination with free carriers of opposite charge. Such initial decay components are shown to be highly sensitive to remnant charge carriers that accumulate in traps under pulsed‐laser excitation, with partial trap occupation masking the trap density actually present in the material. Finally, such modelling reveals a change in trap density at the phase transition, and disentangles the radiative and nonradiative charge recombination channels present in FA0.95Cs0.05PbI3, accurately predicting the experimentally recorded PL efficiencies between 50 and 295 K, and demonstrating that bimolecular recombination is a fully radiative process

Determination of the top-quark pole mass and strong coupling constant from the ttbar production cross section in pp collisions at sqrt(s) = 7 TeV

The inclusive cross section for top-quark pair production measured by the CMS experiment in proton-proton collisions at a center-of-mass energy of 7 TeV is compared to the QCD prediction at next-to-next-to-leading order with various parton distribution functions to determine the top-quark pole mass, mtpole, or the strong coupling constant, alphaS. With the parton distribution function set NNPDF2.3, a pole mass of 176.7 +3.8 -3.4 GeV is obtained when constraining alphaS at the scale of the Z boson mass, mZ, to the current world average. Alternatively, by constraining mtpole to the latest average from direct mass measurements, a value of alphaS(mZ) = 0.1151 +0.0033 -0.0032 is extracted. This is the first determination of alphaS using events from top-quark production

Description and performance of track and primary-vertex reconstruction with the CMS tracker

A description is provided of the software algorithms developed for the CMS tracker both for reconstructing charged-particle trajectories in proton-proton interactions and for using the resulting tracks to estimate the positions of the LHC luminous region and individual primary-interaction vertices. Despite the very hostile environment at the LHC, the performance obtained with these algorithms is found to be excellent. For t (t) over bar events under typical 2011 pileup conditions, the average track-reconstruction efficiency for promptly-produced charged particles with transverse momenta of p(T) > 0.9GeV is 94% for pseudorapidities of vertical bar eta vertical bar < 0.9 and 85% for 0.9 < vertical bar eta vertical bar < 2.5. The inefficiency is caused mainly by hadrons that undergo nuclear interactions in the tracker material. For isolated muons, the corresponding efficiencies are essentially 100%. For isolated muons of p(T) = 100GeV emitted at vertical bar eta vertical bar < 1.4, the resolutions are approximately 2.8% in p(T), and respectively, 10 m m and 30 mu m in the transverse and longitudinal impact parameters. The position resolution achieved for reconstructed primary vertices that correspond to interesting pp collisions is 10-12 mu m in each of the three spatial dimensions. The tracking and vertexing software is fast and flexible, and easily adaptable to other functions, such as fast tracking for the trigger, or dedicated tracking for electrons that takes into account bremsstrahlung

Alignment of the CMS tracker with LHC and cosmic ray data

The central component of the CMS detector is the largest silicon tracker ever built. The precise alignment of this complex device is a formidable challenge, and only achievable with a significant extension of the technologies routinely used for tracking detectors in the past. This article describes the full-scale alignment procedure as it is used during LHC operations. Among the specific features of the method are the simultaneous determination of up to 200 000 alignment parameters with tracks, the measurement of individual sensor curvature parameters, the control of systematic misalignment effects, and the implementation of the whole procedure in a multiprocessor environment for high execution speed. Overall, the achieved statistical accuracy on the module alignment is found to be significantly better than 10 mu m