arXiv subjects
Publications and source records attributed to Andreas Maier.
The top-quark mass can be determined with very high precision from a scan of the total $e^+e^-\to b\bar{b}W^+W^-X$ cross section near the top-pair production threshold. We present the full calculation of electroweak and non-resonant corrections to this process at NNLO. We discuss the size of the new contributions and estimate the theory uncertainty on the top-quark mass in the PS mass scheme.
We focus on a non-abelian gauge field coupled to a single (but general) representation of a family of Nf fermions. By using the same machinery that had allowed us to evaluate the sub-leading large-Nf term of the five-loop Beta function earlier, we here report on a confirmation of the all-Nf result that has in the meantime been published by another group. Furthermore, in order to push forward the 5-loop renormalization program regarding gauge parameter dependence, we present the linear terms of the complete set of anomalous dimensions, in an expansion in the covariant gauge parameter around the Feynman gauge.
Automatic multi-organ segmentation of the dual energy computed tomography (DECT) data can be beneficial for biomedical research and clinical applications. However, it is a challenging task. Recent advances in deep learning showed the feasibility to use 3-D fully convolutional networks (FCN) for voxel-wise dense predictions in single energy computed tomography (SECT). In this paper, we proposed a 3D FCN based method for automatic multi-organ segmentation in DECT. The work was based on a cascaded FCN and a general model for the major organs trained on a large set of SECT data. We preprocessed the DECT data by using linear weighting and fine-tuned the model for the DECT data. The method was evaluated using 42 torso DECT data acquired with a clinical dual-source CT system. Four abdominal organs (liver, spleen, left and right kidneys) were evaluated. Cross-validation was tested. Effect of the weight on the accuracy was researched. In all the tests, we achieved an average Dice coefficient of 93% for the liver, 90% for the spleen, 91% for the right kidney and 89% for the left kidney, respectively. The results show our method is feasible and promising.
We update the experimental moments for the charm quark as computed in arXiv:hep-ph/0702103. and used in arXiv:0907.2110 and arXiv:1010.6157 for the determination of the charm-quark mass. The new value for the MSbar charm-quark mass reads mc(3GeV)=0.993+/-0.008 GeV.
For complex segmentation tasks, fully automatic systems are inherently limited in their achievable accuracy for extracting relevant objects. Especially in cases where only few data sets need to be processed for a highly accurate result, semi-automatic segmentation techniques exhibit a clear benefit for the user. One area of application is medical image processing during an intervention for a single patient. We propose a learning-based cooperative segmentation approach which includes the computing entity as well as the user into the task. Our system builds upon a state-of-the-art fully convolutional artificial neural network (FCN) as well as an active user model for training. During the segmentation process, a user of the trained system can iteratively add additional hints in form of pictorial scribbles as seed points into the FCN system to achieve an interactive and precise segmentation result. The segmentation quality of interactive FCNs is evaluated. Iterative FCN approaches can yield superior results compared to networks without the user input channel component, due to a consistent improvement in segmentation quality after each interaction.
This document contains the results obtained by the Innovation Management Seminar in winter term 2017. In total 11 ideas have been developed by the team. In the document all 11 ideas show improvements for future applications in ophthalmology. The 11 ideas are AR/VR Glasses with Medical Applications, Augmented Reality Eye Surgery, Game Diagnosis, Intelligent Adapting Glasses, MD Facebook, Medical Crowd Segmentation, Personalized 3D Model of the Human Eye, Photoacoustic Contact Lens, Power Supply Smart Contact Lens, VR-Cornea and Head Mount for Fundus Imaging
Deep Convolutional Neural Networks (CNN) have shown great success in supervised classification tasks such as character classification or dating. Deep learning methods typically need a lot of annotated training data, which is not available in many scenarios. In these cases, traditional methods are often better than or equivalent to deep learning methods. In this paper, we propose a simple, yet effective, way to learn CNN activation features in an unsupervised manner. Therefore, we train a deep residual network using surrogate classes. The surrogate classes are created by clustering the training dataset, where each cluster index represents one surrogate class. The activations from the penultimate CNN layer serve as features for subsequent classification tasks. We evaluate the feature representations on two publicly available datasets. The focus lies on the ICDAR17 competition dataset on historical document writer identification (Historical-WI). We show that the activation features trained without supervision are superior to descriptors of state-of-the-art writer identification methods. Additionally, we achieve comparable results in the case of handwriting classification using the ICFHR16 competition dataset on historical Latin script types (CLaMM16).
Identification and counting of cells and mitotic figures is a standard task in diagnostic histopathology. Due to the large overall cell count on histological slides and the potential sparse prevalence of some relevant cell types or mitotic figures, retrieving annotation data for sufficient statistics is a tedious task and prone to a significant error in assessment. Automatic classification and segmentation is a classic task in digital pathology, yet it is not solved to a sufficient degree. We present a novel approach for cell and mitotic figure classification, based on a deep convolutional network with an incorporated Spatial Transformer Network. The network was trained on a novel data set with ten thousand mitotic figures, about ten times more than previous data sets. The algorithm is able to derive the cell class (mitotic tumor cells, non-mitotic tumor cells and granulocytes) and their position within an image. The mean accuracy of the algorithm in a five-fold cross-validation is 91.45%. In our view, the approach is a promising step into the direction of a more objective and accurate, semi-automatized mitosis counting supporting the pathologist.
We calculate the two-loop contributions from a modified trilinear Higgs self-interaction, $κ_λλ_{\rm SM} v h^3$, to the electroweak oblique parameters $S$ and $T$. Using the current bounds on $S$ and $T$ from electroweak measurements, we find the 95% C.L. constraint on the modified trilinear coupling to be $-14.0 \leq κ_λ\leq 17.4$. The largest effects on $S$ and $T$ arise from two insertions of the modified trilinear coupling that result in $T/ S \simeq -3/2$; remarkably, this is nearly parallel to the axis of the tightest experimental constraint in the $S$-$T$ plane. No contributions to $S$ and $T$ arise from a modified Higgs quartic coupling at two-loop order. These calculations utilized a gauge-invariant parameterization of the trilinear Higgs coupling in terms of higher dimensional operators $(H^\dagger H)^{n}$ with $n \ge 3$. Interestingly, the bounds on $κ_λ$ that we obtain are comparable to constraints from di-Higgs production at the LHC as well as recent bounds from single Higgs production at the LHC.
Oral Squamous Cell Carcinoma (OSCC) is a common type of cancer of the oral epithelium. Despite their high impact on mortality, sufficient screening methods for early diagnosis of OSCC often lack accuracy and thus OSCCs are mostly diagnosed at a late stage. Early detection and accurate outline estimation of OSCCs would lead to a better curative outcome and an reduction in recurrence rates after surgical treatment. Confocal Laser Endomicroscopy (CLE) records sub-surface micro-anatomical images for in vivo cell structure analysis. Recent CLE studies showed great prospects for a reliable, real-time ultrastructural imaging of OSCC in situ. We present and evaluate a novel automatic approach for a highly accurate OSCC diagnosis using deep learning technologies on CLE images. The method is compared against textural feature-based machine learning approaches that represent the current state of the art. For this work, CLE image sequences (7894 images) from patients diagnosed with OSCC were obtained from 4 specific locations in the oral cavity, including the OSCC lesion. The present approach is found to outperform the state of the art in CLE image recognition with an area under the curve (AUC) of 0.96 and a mean accuracy of 88.3% (sensitivity 86.6%, specificity 90%).
We present new analytical five-loop Feynman-gauge results for the anomalous dimensions of ghost field and -vertex, generalizing the known values for SU(3) to a general gauge group. Together with previously published results on the quark mass and -field anomalous dimensions and the Beta function, this completes the 5-loop renormalization program of gauge theories in that gauge.
We present analytical five-loop results for the quark mass and quark field anomalous dimensions, for a general gauge group and in the MSbar scheme. We confirm the values known for the gauge group SU(3) from an independent calculation, and find full agreement with results available from large-Nf studies.
When a measurement falls outside the quantization or measurable range, it becomes saturated and cannot be used in classical reconstruction methods. For example, in C-arm angiography systems, which provide projection radiography, fluoroscopy, digital subtraction angiography, and are widely used for medical diagnoses and interventions, the limited dynamic range of C-arm flat detectors leads to overexposure in some projections during an acquisition, such as imaging relatively thin body parts (e.g., the knee). Aiming at overexposure correction for computed tomography (CT) reconstruction, we in this paper propose a mixed one-bit compressive sensing (M1bit-CS) to acquire information from both regular and saturated measurements. This method is inspired by the recent progress on one-bit compressive sensing, which deals with only sign observations. Its successful applications imply that information carried by saturated measurements is useful to improve recovery quality. For the proposed M1bit-CS model, alternating direction methods of multipliers is developed and an iterative saturation detection scheme is established. Then we evaluate M1bit-CS on one-dimensional signal recovery tasks. In some experiments, the performance of the proposed algorithms on mixed measurements is almost the same as recovery on unsaturated ones with the same amount of measurements. Finally, we apply the proposed method to overexposure correction for CT reconstruction on a phantom and a simulated clinical image. The results are promising, as the typical streaking artifacts and capping artifacts introduced by saturated projection data are effectively reduced, yielding significant error reduction compared with existing algorithms based on extrapolation.
Motion estimation across low-resolution frames and the reconstruction of high-resolution images are two coupled subproblems of multi-frame super-resolution. This paper introduces a new joint optimization approach for motion estimation and image reconstruction to address this interdependence. Our method is formulated via non-linear least squares optimization and combines two principles of robust super-resolution. First, to enhance the robustness of the joint estimation, we propose a confidence-aware energy minimization framework augmented with sparse regularization. Second, we develop a tailor-made Levenberg-Marquardt iteration scheme to jointly estimate motion parameters and the high-resolution image along with the corresponding model confidence parameters. Our experiments on simulated and real images confirm that the proposed approach outperforms decoupled motion estimation and image reconstruction as well as related state-of-the-art joint estimation algorithms.
We present analytical results for the $N_f^4$ and $N_f^3$ terms of the five-loop Beta function, for a general gauge group. While the former term agrees with results available from large-$N_f$ studies, the latter is new and extends the value known for SU(3) from an independent calculation.
The acquisition of high-resolution retinal fundus images with a large field of view (FOV) is challenging due to technological, physiological and economic reasons. This paper proposes a fully automatic framework to reconstruct retinal images of high spatial resolution and increased FOV from multiple low-resolution images captured with non-mydriatic, mobile and video-capable but low-cost cameras. Within the scope of one examination, we scan different regions on the retina by exploiting eye motion conducted by a patient guidance. Appropriate views for our mosaicing method are selected based on optic disk tracking to trace eye movements. For each view, one super-resolved image is reconstructed by fusion of multiple video frames. Finally, all super-resolved views are registered to a common reference using a novel polynomial registration scheme and combined by means of image mosaicing. We evaluated our framework for a mobile and low-cost video fundus camera. In our experiments, we reconstructed retinal images of up to 30° FOV from 10 complementary views of 15° FOV. An evaluation of the mosaics by human experts as well as a quantitative comparison to conventional color fundus images encourage the clinical usability of our framework.
For augmented fluoroscopy during cardiac catheter ablation procedures, a preoperatively acquired 3-D model of the left atrium of the patient can be registered to X-ray images. Therefore the 3D-model is matched with the contrast agent based appearance of the left atrium. Commonly, only small amounts of contrast agent (CA) are used to locate the left atrium. This is why we focus on robust registration methods that work also if the structure of interest is only partially contrasted. In particular, we propose two similarity measures for CA-based registration: The first similarity measure, explicit apparent edges, focuses on edges of the patient anatomy made visible by contrast agent and can be computed quickly on the GPU. The second novel similarity measure computes a contrast agent distribution estimate (CADE) inside the 3-D model and rates its consistency with the CA seen in biplane fluoroscopic images. As the CADE computation involves a reconstruction of CA in 3-D using the CA within the fluoroscopic images, it is slower. Using a combination of both methods, our evaluation on 11 well-contrasted clinical datasets yielded an error of 7.9+/-6.3 mm over all frames. For 10 datasets with little CA, we obtained an error of 8.8+/-6.7 mm. Our new methods outperform a registration based on the projected shadow significantly (p<0.05).
The mass of the bottom quark can be determined with high precision from moments of the pair-production cross section sigma(e+ e- -> b bbar) near threshold. We present the first complete NNNLO determination from non-relativistic sum rules, obtaining a bottom-quark mass of mb^PS(2 GeV) = (4.532 +0.013 -0.039) GeV in the potential-subtracted scheme. For the mass in the MSbar scheme we find mb^MS(mb^MS) = (4.203 +0.016 -0.034) GeV using the recently computed four-loop correction to the scheme conversion.