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Background Geometric morphometrics is a powerful approach to capture and quantify morphological shape variation. Both 3D digitizer arms and structured light surface scanners are portable, easy to use, and relatively cheap, which makes these two capturing devices obvious choices for geometric morphometrics. While digitizer arms have been the “gold standard”, benefits of having full 3D models are manifold. We assessed the measurement error and investigate bias associated with the use of an open-source, high-resolution structured light scanner called SeeMaLab against the popular Microscribe 3D digitizer arm. Methodology The analyses were based on 22 grey seal (Halichoerus grypus) skulls. 31 fixed anatomical landmarks were annotated both directly using a Microscribe 3D digitizer and on reconstructed 3D digital models created from structured light surface scans. Each skull was scanned twice. Two operators annotated the landmarks, each twice on all the skulls and 3D models, allowing for the investigation of multiple sources of measurement error. We performed multiple Procrustes ANOVAs to compare the two devices in terms of within- and between-operator error, to quantify the measurement error induced by device, to compare between-device error with other sources of variation, and to assess the level of scanning-related error. We investigated the presence of general shape bias due to device and operator. Results Similar precision was obtained with both devices. If landmarks that were identified as less clearly defined and thus harder to place were omitted, the scanner pipeline would achieve higher precision than the digitizer. Between-operator error was biased and seemed to be smaller when using the scanner pipeline. There were systematic differences between devices, which was mainly driven by landmarks less clearly defined. The factors device, operator and landmark replica were all statistically significant and of similar size, but were minor sources of total shape variation, compared to the biological variation among grey seal skulls. The scanning-related error was small compared to all other error sources. Conclusions As the scanner showed precision similar to the digitizer, a scanner should be used if the advantages of obtaining detailed 3D models of a specimen are desired. To obtain high precision, a pre-study should be conducted to identify difficult landmarks. Due to the observed bias, data from different devices and/or operators should not be combined when the expected biological variation is small, without testing the landmarks for repeatability across platforms and operators. For any study necessitating the combination of landmark measurements from different operators, the scanner pipeline will be better suited. The small scanning-related error indicates that by following the same scanning protocol, different operators can be involved in the scanning process without introducing significant error.

Simple Summary Around 15% of breast cancer patients are diagnosed as triple-negative (TNBC), which have significantly lower 5-year survival rates (77%) than other types of breast cancer (93%). Our study aimed at developing an image analysis-based biomarker to assess how the immune system interacts with the tumor and investigate the potential added value of stromal tumor-infiltrating lymphocytes (sTIL) for the prognosis of overall survival compared to the manual approach. In a large retrospective cohort of 257 patients, we found that our fully automated hematoxylin and eosin (H&E) image analysis pipeline can quantify sTIL density showing both high concordance with manual scoring and association with the prognosis of patients with TNBC. It also overcomes natural limitations of manual assessment that hinder clinical adoption of the immune biomarker. We conclude that sTIL scoring by automated image analysis has prognostic potential comparable to manual scoring and should be further investigated for future use in a clinical setting. Abstract Triple-negative breast cancer (TNBC) is an aggressive and difficult-to-treat cancer type that represents approximately 15% of all breast cancers. Recently, stromal tumor-infiltrating lymphocytes (sTIL) resurfaced as a strong prognostic biomarker for overall survival (OS) for TNBC patients. Manual assessment has innate limitations that hinder clinical adoption, and the International Immuno-Oncology Biomarker Working Group (TIL-WG) has therefore envisioned that computational assessment of sTIL could overcome these limitations and recommended that any algorithm should follow the manual guidelines where appropriate. However, no existing studies capture all the concepts of the guideline or have shown the same prognostic evidence as manual assessment. In this study, we present a fully automated digital image analysis pipeline and demonstrate that our hematoxylin and eosin (H&E)-based pipeline can provide a quantitative and interpretable score that correlates with the manual pathologist-derived sTIL status, and importantly, can stratify a retrospective cohort into two significant distinct prognostic groups. We found our score to be prognostic for OS (HR: 0.81 CI: 0.72–0.92 p = 0.001) independent of age, tumor size, nodal status, and tumor type in statistical modeling. While prior studies have followed fragments of the TIL-WG guideline, our approach is the first to follow all complex aspects, where appropriate, supporting the TIL-WG vision of computational assessment of sTIL in the future clinical setting.

Light sheet microscopy of fluorescently labelled and optically cleared intact organs is a novel approach which enables to visualize structure and function of the brain in 3D. The methodology is gaining popularity in the neuroscience community and dedicated algorithms are being developed for segmenting and quantifying different neurological markers. However, comparisons of marker characteristics between the study groups are conventionally performed by conducting statistical testing for every atlas-defined brain region. While this statistical approach yields viable results, it is biased by the region delineations and does not provide information on the signal properties at a voxel level. In this work, we convert the 3D histological signal from segmented c-Fos + cells into a format suitable for conducting voxel-wise group comparisons and demonstrate the potential of a recent technique, probabilistic threshold-free cluster enhancement method, in a brief comparative study of six different approaches to voxel-based statistical analysis.

eLife 10, e71359 (2021). doi:10.7554/eLife.71359, For the first time, we have used phase-contrast X-ray tomography to characterize the three-dimensional (3d) structure of cardiac tissue from patients who succumbed to Covid-19. By extending conventional histopathological examination by a third dimension, the delicate pathological changes of the vascular system of severe Covid-19 progressions can be analyzed, fully quantified and compared to other types of viral myocarditis and controls. To this end, cardiac samples with a cross-section of 3.5mm were scanned at a laboratory setup as well as at a parallel beam setup at a synchrotron radiation facility the synchrotron in a parallel beam configuration. The vascular network was segmented by a deep learning architecture suitable for 3d datasets (V-net), trained by sparse manual annotations. Pathological alterations of vessels, concerning the variation of diameters and the amount of small holes, were observed, indicative of elevated occurrence of intussusceptive angiogenesis, also confirmed by high-resolution cone beam X-ray tomography and scanning electron microscopy. Furthermore, we implemented a fully automated analysis of the tissue structure in the form of shape measures based on the structure tensor. The corresponding distributions show that the histopathology of Covid-19 differs from both influenza and typical coxsackie virus myocarditis., Published by eLife Sciences Publications, Cambridge

Understanding the diversity of eyes is crucial to unravel how different animals use vision to interact with their respective environments. To date, comparative studies of eye anatomy are scarce because they often involve time-consuming or inefficient methods. X-ray micro-tomography is a promising high-throughput imaging technique that enables to reconstruct the 3D anatomy of eyes, but powerful tools are needed to perform fast conversions of anatomical reconstructions into functional eye models. We developed a computing method named InSegtCone to automatically segment the crystalline cones in the apposition compound eyes of arthropods. Here, we describe the full auto-segmentation process, showcase its application to three different insect compound eyes and evaluate its performance. The auto-segmentation could successfully label the full individual shapes of 60%-80% of the crystalline cones, and is about as accurate and 250 times faster than manual labelling of the individual cones. We believe that InSegtCone can be an important tool for peer scientists to enable extensive comparisons of the diversity of eyes and vision in arthropods.

Image-based phenotype data with high temporal resolution offers advantages over end-point measurements in plant quantitative genetics experiments, because growth dynamics can be assessed and analysed for genotype-phenotype association. Recently, network-based camera systems have been deployed as customizable, low-cost phenotyping solutions. Here, we implemented a large, automated image-capture system based on distributed computing using 180 networked Raspberry Pi units that could simultaneously monitor 1,800 white clover (Trifolium repens) plants. The camera system proved stable with an average uptime of 96% across all 180 cameras. For analysis of the captured images, we developed the Greenotyper image analysis pipeline. It detected the location of the plants with a bounding box accuracy of 97.98%, and the U-net-based plant segmentation had an intersection over union accuracy of 0.84 and a pixel accuracy of 0.95. We used Greenotyper to analyze a total of 355,027 images, which required 24–36 h. Automated phenotyping using a large number of static cameras and plants thus proved a cost-effective alternative to systems relying on conveyor belts or mobile cameras.

Geometrical defects on the surface of wind turbine blades can severely degrade the blade during operation and lead to reduced lift, which in turn reduces the power output of the turbine. This paper presents an automated surface geometry inspection system, which is designed based on manufacturing requirements. Estimating the measurement uncertainty and establishing traceability is difficult for huge, freeform objects. An approach based on the Modular Freeform Gauge (MFG) method is presented and used to estimate the measurement uncertainty of the system. An expanded measurement uncertainty of 665 μm ( k = 2 ) was established for the system and verified by measurements on a 55 meter long blade.

For 3D images, segmentation via fitting surface meshes to object boundaries provides an efficient way to handle large images and enforce geometric prior knowledge. Furthermore, fitting such meshes with graph cuts has proven to be a versatile and robust framework. However, when segmenting multiple distinct objects in one image, current methods do not allow the natural constraint that objects should not overlap. In this paper, we present an extension to graph cut based methods which can provide a globally optimal segmentation of thousands of objects while guaranteeing no overlap. Our method works by separating objects with planes whose positions are determined as part of the graph cut. To demonstrate the general applicability of our method, we apply it to several 3D microscopy data sets from both biology and materials science. Our results show both quantitative and qualitative improvements.

We introduce the novel concept of a Sparse Layered Graph (SLG) for s-t graph cut segmentation of image data. The concept is based on the widely used Ishikawa layered technique for multi-object segmentation, which allows explicit object interactions, such as containment and exclusion with margins. However, the spatial complexity of the Ishikawa technique limits its use for many segmentation problems. To solve this issue, we formulate a general method for adding containment and exclusion interaction constraints to layered graphs. Given some prior knowledge, we can create a SLG, which is often orders of magnitude smaller than traditional Ishikawa graphs, with identical segmentation results. This allows us to solve many problems that could previously not be solved using general graph cut algorithms. We then propose three algorithms for further reducing the spatial complexity of SLGs, by using ordered multi-column graphs. In our experiments, we show that SLGs, and in particular ordered multi-column SLGs, can produce high-quality segmentation results using extremely simple data terms. We also show the scalability of ordered multi-column SLGs, by segmenting a high-resolution volume with several hundred interacting objects.

Automated detection of cancer metastases in lymph nodes has the potential to improve the assessment of prognosis for patients. To enable fair comparison between the algorithms for this purpose, we set up the CAMELYON17 challenge in conjunction with the IEEE International Symposium on Biomedical Imaging 2017 Conference in Melbourne. Over 300 participants registered on the challenge website, of which 23 teams submitted a total of 37 algorithms before the initial deadline. Participants were provided with 899 whole-slide images (WSIs) for developing their algorithms. The developed algorithms were evaluated based on the test set encompassing 100 patients and 500 WSIs. The evaluation metric used was a quadratic weighted Cohen's kappa. We discuss the algorithmic details of the 10 best pre-conference and two post-conference submissions. All these participants used convolutional neural networks in combination with pre- and postprocessing steps. Algorithms differed mostly in neural network architecture, training strategy, and pre- and postprocessing methodology. Overall, the kappa metric ranged from 0.89 to -0.13 across all submissions. The best results were obtained with pre-trained architectures such as ResNet. Confusion matrix analysis revealed that all participants struggled with reliably identifying isolated tumor cells, the smallest type of metastasis, with detection rates below 40%. Qualitative inspection of the results of the top participants showed categories of false positives, such as nerves or contamination, which could be targeted for further optimization. Last, we show that simple combinations of the top algorithms result in higher kappa metric values than any algorithm individually, with 0.93 for the best combination.

The fermentation process of salamis involves several parameters influencing taste, texture, and color of the salami. One significant parameter is the fermentation time. It is difficult to conduct sensory evaluations to assess the effect of time without introducing variation between observation days. It may be possible to overcome this by stalling or pausing the fermentation by deep-chilling the salamis. This study investigates the difference of non- and deep-chilled salamis with the use of a multispectral imaging system. The statistical investigation, based on image features relating to size, visual texture, and color of the sausages over time, showed that it may be possible to stall the fermentation process. It was shown that a statistical difference in the two kinds of samples is present. For the size feature the difference could be quantified into a number of days. However, for the important color feature only a statistical difference was observed, whereas the visual difference expressed in terms of ΔEab ⁎ was barely present.

This study investigates the use of fractal algorithms to analyse MRI of meat products, specifically loin, in order to determine sensory parameters of loin. For that, the capability of different fractal algorithms was evaluated (Classical Fractal Algorithm, CFA; Fractal Texture Algorithm, FTA and One Point Fractal Texture Algorithm, OPFTA). Moreover, the influence of the acquisition sequence of MRI (Gradient echo, GE; Spin Echo, SE and Turbo 3D, T3D) and the predictive technique of data mining (Isotonic regression, IR and Multiple Linear regression, MLR) on the accuracy of the prediction was analysed. Results on this study firstly demonstrate the capability of fractal algorithms to analyse MRI from meat product. Different combinations of the analysed techniques can be applied for predicting most sensory attributes of loins adequately (R > 0.5). However, the combination of SE, OPFTA and MLR offered the most appropriate results. Thus, it could be proposed as an alternative to the traditional food technology methods.

A unidirectional (UD) glass fibre reinforced polymer (GFRP) composite was scanned at varying resolutions in the micro-scale with several imaging modalities. All six scans capture the same region of the sample, containing well-aligned fibres inside a UD load-carrying bundle. Two scans of the cross-sectional surface of the bundle were acquired at a high resolution, by means of scanning electron microscopy (SEM) and optical microscopy (OM), and four volumetric scans were acquired through X-ray computed tomography (CT) at different resolutions. Individual fibres can be resolved from these scans to investigate the micro-structure of the UD bundle. The data is hosted at https://doi.org/10.5281/zenodo.1195879 and it was used in Emerson et al. (2018) [1] to demonstrate that precise and representative characterisations of fibre geometry are possible with relatively low X-ray CT resolutions if the analysis method is robust to image quality. Keywords: Geometrical characterisation, Polymer-matrix composites (PMCs), Volumetric fibre segmentation, Automated fibre tracking, X-ray imaging, Microscopy, Non-destructive testing

Imaging with X-ray computed tomography (CT) enables non-destructive 3D characterisations of the micro-structure inside fibre composites. In this paper we validate the use of X-ray CT coupled with image analysis for characterising unidirectional (UD) fibre composites. We compare X-ray CT at different resolutions to optical microscopy (OM) and scanning electron microscopy (SEM), where we characterise fibres by their diameters and positions. In addition to comparing individual fibre diameters, we also model their spatial distribution, and compare the obtained model parameters. Our study shows that X-ray CT is a high precision technique for characterising fibre composites and, with our suggested image analysis method for fibre detection, high precision is also obtained at low resolutions. This has great potential, since it allows larger fields of view to be analysed. Besides analysing representative volumes with high precision, we demonstrate that based on our methodology for individual fibre segmentation it is now possible to study complete bundles at the fibre scale and reveal inhomogeneities in the physical sample.

This work firstly investigates the use of MRI, fractal algorithms and data mining techniques to determine pork quality parameters non-destructively. The main objective was to evaluate the capability of fractal algorithms (Classical Fractal algorithm, CFA; Fractal Texture Algorithm, FTA and One Point Fractal Texture Algorithm, OPFTA) to analyse MRI in order to predict quality parameters of loin. In addition, the effect of the sequence acquisition of MRI (Gradient echo, GE; Spin echo, SE and Turbo 3D, T3D) and the predictive technique of data mining (Isotonic regression, IR and Multiple linear regression, MLR) were analysed. Both fractal algorithm, FTA and OPFTA are appropriate to analyse MRI of loins. The sequence acquisition, the fractal algorithm and the data mining technique seems to influence on the prediction results. For most physico-chemical parameters, prediction equations with moderate to excellent correlation coefficients were achieved by using the following combinations of acquisition sequences of MRI, fractal algorithms and data mining techniques: SE-FTA-MLR, SE-OPFTA-IR, GE-OPFTA-MLR, SE-OPFTA-MLR, with the last one offering the best prediction results. Thus, SE-OPFTA-MLR could be proposed as an alternative technique to determine physico-chemical traits of fresh and dry-cured loins in a non-destructive way with high accuracy.

Background: In recent years, a variety of new machine learning methods are being employed in prediction of disease progression, e.g. random forest or neural networks, but how do they compare to and are they direct substitutes for the more traditional statistical methods like the Cox proportional hazards model? In this paper, we compare three of the most commonly used approaches to model prediction of disease progression. We consider a type 2 diabetes case from a cohort-based population in Tayside, UK. In this study, the time until a patient goes onto insulin treatment is of interest; in particular discriminating between slow and fast progression. This means that we are both interested in the results as a raw time-to-insulin prediction but also in a dichotomized outcome making the prediction a classification.Methods: Three different methods for prediction are considered: A Cox proportional hazards model, random forest for survival data and a neural network on the dichotomized outcome. The performance is evaluated using survival performance measures (concordance indices and the integrated Brier score) and using the accuracy, sensitivity, specificity, and Matthews correlation.coefficient for the corresponding classification problems.Results: We found no improvement when using the conditional inference forest over the Cox model. The neural network out performed the conditional inference forest in the classification problem. We discuss the limitations of the three approaches and where they each excel in terms of prediction performance, interpretation, and how they handle data imbalance.

Abnormal blood compositions can lead to abnormal blood flow which can influence the macular vasculature. Optical coherence tomography angiography (OCTA) makes it possible to study the macular vasculature and potential vascular abnormalities induced by hematological disorders. Here, we investigate vascular changes in control subjects and in hematologic patients before and after treatment. Since these changes are small, they are difficult to notice in the OCTA images. To quantify vascular changes, we propose a method for combined capillary registration, dictionary-based segmentation and local density estimation. Using this method, we investigate three patients and five controls, and our results show that we can detect small changes in the vasculature in patients with large changes in blood composition.

Efficient and easy segmentation of images and volumes is of great practical importance. Segmentation problems that motivate our approach originate from microscopy imaging commonly used in materials science, medicine, and biology. We formulate image segmentation as a probabilistic pixel classification problem, and we apply segmentation as a step towards characterising image content. Our method allows the user to define structures of interest by interactively marking a subset of pixels. Thanks to the real-time feedback, the user can place new markings strategically, depending on the current outcome. The final pixel classification may be obtained from a very modest user input. An important ingredient of our method is a graph that encodes image content. This graph is built in an unsupervised manner during initialisation and is based on clustering of image features. Since we combine a limited amount of user-labelled data with the clustering information obtained from the unlabelled parts of the image, our method fits in the general framework of semi-supervised learning. We demonstrate how this can be a very efficient approach to segmentation through pixel classification., 9 pages, 7 figures, PDFLaTeX

Deep learning-based algorithms have shown great promise for assisting pathologists in detecting lymph node metastases when evaluated based on their predictive accuracy. However, for clinical adoption, we need to know what happens when the test set dramatically changes from the training distribution. In such settings, we should estimate the uncertainty of the predictions, so we know when to trust the model (and when not to). Here, we i) investigate current popular methods for improving the calibration of predictive uncertainty, and ii) compare the performance and calibration of the methods under clinically relevant in-distribution dataset shifts. Furthermore, we iii) evaluate their performance on the task of out-of-distribution detection of a different histological cancer type not seen during training. Of the investigated methods, we show that deep ensembles are more robust in respect of both performance and calibration for in-distribution dataset shifts and allows us to better detect incorrect predictions. Our results also demonstrate that current methods for uncertainty quantification are not necessarily able to detect all dataset shifts, and we emphasize the importance of monitoring and controlling the input distribution when deploying deep learning for digital pathology.

Investigations in how the retinal microvasculature correlates with ophthalmological conditions necessitate a method for measuring the microvasculature. Optical coherence tomography angiography (OCTA) depicts the superficial and the deep layer of the retina, but quantification of the microvascular network is still needed. Here, we propose an automatic quantitative analysis of the retinal microvasculature. We use a dictionary-based segmentation to detect larger vessels and capillaries in the retina and we extract features such as densities and vessel radius. The method is validated on repeated OCTA scans from healthy subjects, and we observe high intraclass correlation coefficients and high agreement in a Bland-Altman analysis. The quantification method is also applied to pre- and postoperative scans of cataract patients. Here, we observe a higher variation between the measurements, which can be explained by the greater variation in scan quality. Statistical tests of both the healthy subjects and cataract patients show that our method is able to differentiate subjects based on the extracted microvascular features.

Summary A method is presented, which allows quantification of the roughness of nonplanar boundaries of objects for which the neutral plane is not known. The method provides quantitative descriptions of both the local and global characteristics. How the method can be used to estimate the sizes of rough features and local curvatures is also presented. The potential of the method is illustrated by quantification of the roughness of two recrystallization boundaries in a pure Al specimen characterized by scanning electron microscopy.

Timely detection of surface damages on wind turbine blades is imperative for minimizing downtime and avoiding possible catastrophic structural failures. With recent advances in drone technology, a large number of high-resolution images of wind turbines are routinely acquired and subsequently analyzed by experts to identify imminent damages. Automated analysis of these inspection images with the help of machine learning algorithms can reduce the inspection cost. In this work, we develop a deep learning-based automated damage suggestion system for subsequent analysis of drone inspection images. Experimental results demonstrate that the proposed approach can achieve almost human-level precision in terms of suggested damage location and types on wind turbine blades. We further demonstrate that for relatively small training sets, advanced data augmentation during deep learning training can better generalize the trained model, providing a significant gain in precision.

Timely detection of surface damages on wind turbine blades is imperative for minimising downtime and avoiding possible catastrophic structural failures. With recent advances in drone technology, a large number of high-resolution images of wind turbines are routinely acquired and subsequently analysed by experts to identify imminent damages. Automated analysis of these inspection images with the help of machine learning algorithms can reduce the inspection cost, thereby reducing the overall maintenance cost arising from the manual labour involved. In this work, we develop a deep learning based automated damage suggestion system for subsequent analysis of drone inspection images. Experimental results demonstrate that the proposed approach could achieve almost human level precision in terms of suggested damage location and types on wind turbine blades. We further demonstrate that for relatively small training sets advanced data augmentation during deep learning training can better generalise the trained model providing a significant gain in precision.

We propose a deformable model for manually initialized segmentation of images, which may contain both textured and non-textured regions. Image segments and segment boundaries are represented using a deformable triangle mesh, providing all advantages of an explicit geometry representation, but allowing for adaptive topology. Deformation forces are computed using a probabilistic model of local self-similarity, based on clustering of image patches. Both our curve representation and our similarity model naturally support multi-label segmentation. We demonstrate the properties of our approach on a number of natural color images as well as composed textured images.

The measurement of a bidirectional reflectance distribution function (BRDF) requires a purpose-built instrument. We ease this requirement by presenting a relative method for measuring a BRDF using a multipurpose robotic arm. Our focus is on the alignment of the system to perform accurate camera positioning and orientation. We use a six degrees of freedom robotic arm to move a camera on a hemisphere surrounding a flat material sample. Point-like light sources, fixed on a quarter circle arc, sequentially illuminate the sample from different directions. The resulting images are used to reconstruct the material BRDF. We limit ourselves to tristimulus (RGB) isotropic BRDF acquisition.

We present a data-driven proof of concept method for image-based semantic segmentation of objects based on their materials. We target materials with complex radiometric appearances, such as reflective and refractive materials, as their detection is particularly challenging in many modern vision systems. Specifically, we select glass, chrome, plastic, and ceramics as these often appear in real-world settings. A large dataset of synthetic images is generated with the Blender 3D creation suite and the Cycles renderer. We use this data to fine-tune the pre-trained DeepLabv3+ semantic segmentation convolutional neural network. The network performs well on rendered test data and, although trained with rendered images only, the network generalizes so that the four selected materials can be segmented from real photos.

Faithful representation of pairwise pixel affinities is crucial for the outcome of spectral segmentation methods. In conventional affinity models only close-range pixels interact, and a variety of subsequent techniques aims at faster propagation of local grouping cues across long-range connections. In this paper we propose a general framework for constructing a full-range affinity matrix. Our affinity matrix consists of a global similarity matrix and an additive proximity matrix. The similarity in appearance, including intensity and texture, is encoded for each pair of image pixels. Despite being full-range, our similarity matrix has a simple decomposition, which exploits an assignment of image pixels to dictionary elements. The additive proximity enforces smoothness to the segmentation by imposing interactions between near-by pixels. Our approach allows us to assess the advantages of using a full-range affinity for various spectral segmentation problems. Within our general framework we develop a few variants of full affinity for experimental validation. The performance we accomplish on composite textured images is excellent, and the results on natural images are promising.

Background The risk of infective endocarditis (IE) is markedly increased in patients receiving chronic hemodialysis compared with the general population, but outcome data are sparse. The present study investigated causes and risk factors of mortality in a hemodialysis-treated end-stage kidney disease- (ESKD) and a non-ESKD population with staphylococcus (S.) aureus endocarditis. Methods Hemodialysis-treated ESKD patients with S. aureus endocarditis were identified from Danish National Registries and Non-ESKD patients from The East Danish Database on Endocarditis. For establishing the cause of death The Danish Registry of Cause of Death was used. Independent risk factors of outcome were identified in multivariable Cox regression models. Results One hundred twenty-one hemodialysis patients and 190 non-ESKD patients with S. aureus endocarditis were included during 1996–2012 and 2002–2012, respectively. The all-cause in-hospital mortality was 22.3% in hemodialysis- and 24.7% in non-ESKD patients. One-year mortality, excluding in-hospital mortality, was 26.4% in hemodialysis patients and 15.2% in non-ESKD patients. The hazard ratio of all-cause mortality in hemodialysis was 2.64 (95% CI 1.70–4.10) at > 70 days after admission compared with non-ESKD. Age (HR 1.03 (95% CI 1.02–1.04)) and diabetes mellitus (HR 2.17 (95% CI 1.54–3.10)) were independent risk factors of all-cause mortality. The hazard ratio of cardiovascular death in hemodialysis was 3.20 (95% CI 1.78–5.77) at > 81 days after admission compared with non-ESKD. Age and diabetes mellitus were independently related to cardiovascular death. Conclusion All-cause in-hospital mortality rates were similar in hemodialysis and non-ESKD patients with S. aureus endocarditis whereas one-year mortality rates were significantly increased in the hemodialysis population. Electronic supplementary material The online version of this article (10.1186/s12882-018-1016-0) contains supplementary material, which is available to authorized users.

Background Synchrotron X-ray computed tomography (SXCT) allows for three-dimensional imaging of objects at a very high resolution and in large field-of-view. Purpose The aim of this study was to use SXCT imaging for morphological analysis of muscle tissue, in order to investigate whether the analysis reveals complementary information to two-dimensional microscopy. Methods Three-dimensional SXCT images of muscle biopsies were taken from participants with cerebral palsy and from healthy controls. We designed morphological measures from the two-dimensional slices and three-dimensional volumes of the images and measured the muscle fibre organization, which we term orientation consistency. Results The muscle fibre cross-sectional areas were significantly larger in healthy participants than in participants with cerebral palsy when carrying out the analysis in three dimensions. However, a similar analysis carried out in two dimensions revealed no patient group difference. The present study also showed that three-dimensional orientation consistency was significantly larger for healthy participants than for participants with cerebral palsy. Conclusion Individuals with CP have smaller muscle fibres than healthy control individuals. We argue that morphometric measures of muscle fibres in two dimensions are generally trustworthy only if the fibres extend perpendicularly to the slice plane, and otherwise three-dimensional aspects should be considered. In addition, the muscle tissue of individuals with CP showed a decreased level of orientation consistency when compared to healthy control tissue. We suggest that the observed disorganization of the tissue may be induced by atrophy caused by physical inactivity and insufficient neural activation.

BACKGROUND: Impaired cardiac function is the main predictor of poor outcome in infective endocarditis (IE). Global longitudinal strain (GLS) derived from two-dimensional strain echocardiography has proven superior in prediction of long-term outcome as compared to left ventricular ejection fraction (LVEF) in valvular disease and heart failure in general. Whether measurements of cardiac deformation can predict survival in patients with IE has not previously been investigated.METHODS: The study included consecutive patients with Duke definite IE who underwent transthoracic and transesophageal echocardiography within 7 days. Clinical and echocardiographic markers associated with 1-year survival were identified using a Cox-proportional hazards model that included propensity adjustment for surgery. Reclassification statistics including receiver operating characteristic curves and net reclassification improvement were applied to LVEF and GLS, respectively.RESULTS: A cohort of 190 patients met eligibility criteria. LVEF and GLS were both prognostic markers of mortality. Independent markers of 1-year mortality were S. aureus IE (HR:2.02; 95%CI 1.11-5.72, p = .022), diabetes (HR:2.05; 95%CI 1.12-3.75, p = .020), embolic stroke (HR:3.95; 95%CI 1.93-8.10, p -15.4% (HR:2.95; 95%CI 1.52-5.72, p CONCLUSIONS: When treatment was taken into account, LVEF -15.4% were both associated with adverse long-term outcome in left-sided IE. GLS >-15.4 % was significantly associated with 1-year mortality in the multivariate analysis. Further, GLS was superior to LVEF in risk prediction and risk discrimination of long-term outcome in patients with left-sided IE.

We are pleased by the interest in our recently published research paper [(1)][1] shown by Dr. Madias as well as Drs. Zhou and Tang. Dr. Madias’s query concerns the interesting hypothesis that antiplatelet treatment might reduce the risk of infective endocarditis (IE). The theory is based on the

We consider tomographic imaging problems where the goal is to obtain both a reconstructed image and a corresponding segmentation. A classical approach is to first reconstruct and then segment the image; more recent approaches use a discrete tomography approach where reconstruction and segmentation are combined to produce a reconstruction that is identical to the segmentation. We consider instead a hybrid approach that simultaneously produces both a reconstructed image and segmentation. We incorporate priors about the desired classes of the segmentation through a Hidden Markov Measure Field Model, and we impose a regularization term for the spatial variation of the classes across neighbouring pixels. We also present an efficient implementation of our algorithm based on state-of-the-art numerical optimization algorithms. Simulation experiments with artificial and real data demonstrate that our combined approach can produce better results than the classical two-step approach.

Through the use of Time-of-Flight Three Dimensional Polarimetric Neutron Tomography (ToF 3DPNT) we have for the first time successfully demonstrated a technique capable of measuring and reconstructing three dimensional magnetic field strengths and directions unobtrusively and non-destructively with the potential to probe the interior of bulk samples which is not amenable otherwise. Using a pioneering polarimetric set-up for ToF neutron instrumentation in combination with a newly developed tailored reconstruction algorithm, the magnetic field generated by a current carrying solenoid has been measured and reconstructed, thereby providing the proof-of-principle of a technique able to reveal hitherto unobtainable information on the magnetic fields in the bulk of materials and devices, due to a high degree of penetration into many materials, including metals, and the sensitivity of neutron polarisation to magnetic fields. The technique puts the potential of the ToF time structure of pulsed neutron sources to full use in order to optimise the recorded information quality and reduce measurement time., Comment: 12 pages, 4 figures

We present a method for virtual unrolling of a thin rolled object. From a volumetric image of the rolled object we obtain a flat image of the object’s surface, which allows visualinspection of the object and has a number of applications. Our method exploits the geometric constrains of the problem and detects a single rolled surface. For surface detection we adapt a solution to an optimal net surface problem, previously used for terrain-like and tubular surfaces. We present our approach on an example of a rolled sheet of microelectronic, which has a layer of flexible polymer substrate and a thin metal layer lithographically coated onto the polymer. Our approach is automatic and robust. The unrolled image is undistorted, and the surface structures may be accurately quantified making our approach a good candidate for an industrial application of virtual unrolling

Recent developments in multispectral X-ray detectors allow for an efficient identification of materials based on their chemical composition. This has a range of applications including security inspection, which is our motivation. In this paper, we analyze data from a tomographic setup employing the MultiX detector, that records projection data in 128 energy bins covering the range from 20 to 160 keV. Obtaining all information from this data requires reconstructing 128 tomograms, which is computationally expensive. Instead, we propose to reduce the dimensionality of projection data prior to reconstruction and reconstruct from the reduced data. We analyze three linear methods for dimensionality reduction using a dataset with 37 equally-spaced projection angles. Four bottles with different materials are recorded for which we are able to obtain similar discrimination of their content using a very reduced subset of tomograms compared to the 128 tomograms that would otherwise be needed without dimensionality reduction.

Background— Because of the nephrotoxic effects of aminoglycosides, the Danish guidelines on infective endocarditis were changed in January 2007, reducing gentamicin treatment in enterococcal infective endocarditis from 4 to 6 weeks to only 2 weeks. In this pilot study, we compare outcomes in patients with Enterococcus faecalis infective endocarditis treated in the years before and after endorsement of these new recommendations. Methods and Results— A total of 84 consecutive patients admitted with definite left-sided E faecalis endocarditis in the period of 2002 to 2011 were enrolled. Forty-one patients were treated before and 43 patients were treated after January 1, 2007. There were no significant differences in baseline characteristics. At hospitalization, the 2 groups had similar estimated glomerular filtration rates of 66 and 75 mL/min ( P =0.22). Patients treated before January 2007 received gentamicin for a significantly longer period (28 versus 14 days; P P =0.75). At discharge, the patients treated before 2007 had a lower estimated glomerular filtration rate (45 versus 66 mL/min; P =0.008) and a significantly greater decrease in estimated glomerular filtration rate (median, 11 versus 1 mL/min; P =0.009) compared with those treated after 2007. Conclusions— Our present pilot study suggests that the recommended 2-week treatment with gentamicin seems adequate and preferable in treating non–high-level aminoglycoside-resistant E faecalis infective endocarditis. The longer duration of gentamicin treatment is associated with worse renal function. Although the certainty of the clinical outcomes is limited by the sample size, outcomes appear to be no worse with the shorter treatment duration. Randomized, controlled studies are warranted to substantiate these results.

Time-efficient acquisition of reflectance behavior together with surface geometry is a challenging problem. In this study, we investigate the impact of system parameter uncertainties when incorporating a data-driven BRDF reconstruction approach into the standard pipeline of a structured light scanning system. The parameters investigated include geometric detail of scanned objects; vertex positions and normals; and position and intensity of light sources. To have full control of uncertainties, experiments are carried out in a simulated environment, mimicking an actual structured light scanning setup. Results show that while uncertainties in vertex positions and normals have a high impact on the quality of reconstructed BRDFs, object geometry and light source properties have very little influence on the reconstructed BRDFs. With this analysis, practitioners now have insight in the tolerances required for accurate BRDF acquisition to work.

The extra-cardiac work-up in infective endocarditis (IE) comprises a search for primary and secondary infective foci. Whether (18)FDG-PET/CT or WBC-SPECT/CT is superior in detection of clinically relevant extra-cardiac manifestations in IE is unexplored. The objectives of this study were to identify the numbers of positive findings detected by each imaging modality, to evaluate the clinical relevance of these findings and to define the reproducibility for extra-cardiac foci in patients with definite IE. Each modality was evaluated for numbers and location of positive extra-cardiac foci in patients with definite IE. A team of 2 × 2 cardiologists evaluated each finding to determine clinical relevance. Clinical utility was determined by 4 criteria converted into an ordinal scale. Using the manifestation with highest clinical utility rating in each patient, the clinical impact of the two imaging modalities was expressed in a clinical utility score. To evaluate reproducibility for each modality, an imaging core laboratory reviewed all findings. In 55 IE patients, 91 pathological foci were found by FDG-PET/CT and 37 foci were identified by WBC-SPECT/CT (p

Non-invasive food inspection and quality assurance are becoming viable techniques in food production due to the introduction of fast and accessible multispectral X-ray scanners. However, the novel devices produce massive amount of data and there is a need for fast and accurate algorithms for processing it. We apply a sparse classifier for foreign object detection and segmentation in multispectral X-ray. Using sparse methods makes it possible to potentially use fewer variables than traditional methods and thereby reduce acquisition time, data volume and classification speed. We report our results on two datasets with foreign objects, one set with spring rolls and one with minced meat. Our results indicate that it is possible to limit the amount of data stored to 50% of the original size without affecting classification accuracy of materials used for training. The method has attractive computational properties, which allows for fast classification of items in new images.

Active Appearance Models (AAM) are used for annotating or segmenting shapes in biomedical images. Performance relies heavily on the image data used to train the AAM. In this paper we improve the generalization properties of the model by making it robust to slowly varying spatial intensity inhomogeneities which are often seen in Light Sheet Fluorescence Microscopy (LSFM) images. This robustness is achieved by modelling the appearance of an image as a regularized Normalized Gradient Field (rNGF). We perform two experiments to challenge the model. First it is tested using a repeated leave-one-out approach on images with minimal imperfections where the left out images are corrupted by a simulated bias field and segmented using the AAM. Secondly we test the model on LSFM images with common acquisition problems. In both experiments the proposed approach outperforms the often used AAM implementation based on Sum of Squared Differences.

OBJECTIVES: To assess the association between comorbidities and Staphylococcus aureus bacteremia in children aged 5-18 years, thus, in children with a matured immune system. Further, we aimed to identify presumably healthy children acquiring bacteremia.STUDY DESIGN: By cross-linking nationwide registries, we consecutively included all children born from 1995 onward at their 5-year birthday or date of immigration during 2000-2015. We examined incidence rate ratios (IRR) between preselected exposures and microbiologically verified S aureus bacteremia (reference = children without exposure) using Poisson regression models.RESULTS: We followed 1 109 169 children in 2000-2015 during which 307 children (incidence rate: 3.7 per 100 000 person-years) acquired S aureus bacteremia (methicillin-resistant S aureus = 8; 2.6%). Children without known comorbidities or recent contact with the healthcare system comprised 37.1% of infected children. The highest IRRs were observed in children undergoing dialysis or plasmapheresis (IRR = 367.2 [95% CI) = 188.5-715.3]), children with organ transplantation (IRR = 149.5 [95% CI = 73.9-302.2]), and children with cancer (IRR = 102.9 [95% CI = 74.4-142.2]). Positive associations also were observed in children with chromosomal anomalies (IRR = 7.16 [95% CI = 2.96-17.34]), atopic dermatitis (IRR = 4.89 [95% CI = 3.11-7.69]), congenital heart disease (IRR = 3.14 [95% CI = 1.92-5.11]), and in children undergoing surgery (IRR = 3.34 [95% CI = 2.59-4.28]). Neither premature birth nor parental socioeconomic status was associated with increased disease rates.CONCLUSIONS: S aureus bacteremia is uncommon in children between 5 and 18 years of age. Risk factors known from the adult population, such as dialysis, plasmapheresis, organ transplantation, and cancer, were associated with the highest relative rates. However, prematurity and parental socioeconomic status were not associated with increased rates. Approximately one-third of infected children were presumably healthy.

BACKGROUND: The NOVA score is a recently developed diagnostic tool to identify patients with increased risk of infective endocarditis (IE) among patients with Enterococcus faecalis (EF) bacteremia. We aim to validate an adapted version of the NOVA score and to identify risk factors for IE in patients with EF bacteremia.METHODS: From January 1, 2010 to December 31, 2013, we included 647 consecutive patients with EF bacteremia. Predictors of IE were identified with multivariable Cox regression. The NOVA score was used in a slightly adapted form; 2/2 positive blood cultures resulted in 5 points, unknown origin of infection 4 points, prior valve disease 2 points and heart murmur 1 point.RESULTS: IE was diagnosed in 78 patients (12%). Monomicrobial EF bacteremia (HR 3.60; CI95% 1.6-8.0), prosthetic heart valve (HR 6.2; CI95% 3.8-10.1), male sex (HR 2.0; CI95% 1.1-3.8), and community acquisition (HR 1.8; CI95% 1.1-2.9) were independently associated with IE. The adapted NOVA score was applied in the 240 patients examined by echocardiography. A low score (CONCLUSION: Monomicrobial EF bacteremia, community acquisition, prosthetic heart valve and male sex are associated with increased risk of IE. In our retrospective cohort, the adapted NOVA score performed well suggesting it could be useful in guiding clinical decisions.

Recovering detailed morphological information from archaeological or paleontological material requires extensive hands-on time. Creating 3D scans based on e.g. computed tomography (CT) will recover the geometry of the specimen, but can inflict bimolecular degradation. Instead, we propose a fast, inoffensive and inexpensive 3D scanning modality based on structured light, suitable for capturing the morphology and the appearance of specimens. Benefits of having 3D models are manifold. The 3D models are easy to share among researchers and can be made available to the general public. Advanced morphological modelling is possible with accurate description of the specimens provided by the models. Furthermore, performing studies on models reduces the risk of damage to the original specimen. In our work we employ a high resolution structured light scanner for digitalizing a collection of 8500 year old human skulls from Brazil. To evaluate the precision of our set-up we compare the structured light scan to micro-CT and achieve sub-millimetre difference. We analyse morphological features of the Brazilian skulls using manual landmarks, but a research goal is to automate this, fully utilize the dense 3D scans, and apply the method to many more samples.

The quality of a dairy product is largely determined by its microstructure which also affects its optical properties. Consequently, an assessment of the optical properties during production may be part of a feedback system for ensuring the quality of the production process. This paper presents a novel camera-based measurement technique that enables robust quantification of a wide range of reduced scattering coefficients and absorption coefficients. Measurements are based on hyperspectral images of diffuse reflectance in the wavelength range of 470 to 1020 nm. The optical properties of commercially available milk and yogurt products with three different levels of fat content are measured. These constitute a relevant range of products at a dairy plant. The measured reduced scattering properties of the samples are presented and show a clear discrimination between levels of fat contents as well as fermentation. The presented measurement technique and method of analysis is thus suitable for a rapid, non-contact, and non-invasive inspection that can deduce physically interpretable properties.

The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues. In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists., Comment: 23 pages, 5 figures, accepted for publication in the journal Medical Image Analysis

We present a method for supervised volumetric segmentationbased on a dictionary of small cubes composed of pairs of intensityand label cubes. Intensity cubes are small image volumes where eachvoxel contains an image intensity. Label cubes are volumes with voxelwiseprobabilities for a given label. The segmentation process is doneby matching a cube from the volume, of the same size as the dictionaryintensity cubes, to the most similar intensity dictionary cube, and fromthe associated label cube we get voxel-wise label probabilities. Probabilitiesfrom overlapping cubes are averaged and hereby we obtain a robustlabel probability encoding. The dictionary is computed from labeled volumetricimage data based on weighted clustering. We experimentallydemonstrate our method using two data sets from material science – aphantom data set of a solid oxide fuel cell simulation for detecting threephases and their interfaces, and a tomogram of a glass fiber compositeused in wind turbine blades for detecting individual glass fibers.

We present a 54-year-old woman with systemic lupus erythematosus (SLE), fever, pericardial effusion and a mitral valve vegetation. (18)F-Fluorodesoxyglucose positron emission tomography CT ((18)F-FDG-PET-CT) showed very high accumulation of the isotope at the mitral valve. The patient underwent cardiothoracic surgery and pathologic examinations showed characteristic morphology of Libman-Sacks vegetations. All microbiological examinations including blood cultures, microscopy, culture and 16s PCR of the valve were negative and the diagnosis of Libman-Sacks endocarditis was convincing. It is difficult to distinguish Libman-Sacks endocarditis from culture-negative infective endocarditis (IE). Molecular imaging techniques are being used increasingly in cases of suspected IE but no studies have previously reported the use in patients with Libman-Sacks endocarditis. In the present case, (18)F-FDG-PET-CT clearly demonstrated the increased glucose uptake caused by infiltrating white blood cells in the ongoing inflammatory process at the mitral valve. In conclusion, (18)F-FDG-PET-CT cannot be used to distinguish between IE and non-infective Libman-Sacks vegetations.

We propose a novel extension to the shape index histogram feature descriptor where the orientation of the second-order curvature is included in the histograms. The orientation of the shape index is reminiscent but not equal to gradient orientation which is widely used for feature description. We evaluate our new feature descriptor using a public dataset consisting of HEp-2 cell images from indirect immunoflourescence lighting. Our results show that we can improve classification performance significantly when including the shape index orientation. Notably, we show that shape index orientation outperforms the gradient orientation on the dataset.

In this paper we present a practical innovation concerning how to achieve high accuracy of camera positioning, when using a 6 axis industrial robots to generate high quality data sets for computer vision. This innovation is based on the realization that to a very large extent the robots positioning error is deterministic, and can as such be calibrated away. We have successfully used this innovation in our efforts for creating data sets for computer vision. Since the use of this innovation has a significant effect on the data set quality, we here present it in some detail, to better aid others in using robots for image data set generation.