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The diseases and injuries of the knee joint are the most common orthopedic disorders. Personalized knee models can be helpful in the process of early intervention and lasting treatment techniques development. Fully automatic reconstruction of knee joint anatomical structures from medical images (CT, MRI, ultrasound) remains a challenge. For this reason, most of state-of-the-art knee joint models contain simplifications such as representation of muscles and ligaments as line segments connecting two points which replace attachment areas. The paper presents algorithms for automatic detection of such points on knee CT images. This paper presents three approaches to automatic detection of ligaments and tendons attachment sites on the patients CT images: qualitative anatomical descriptions, analysis of bones curvature, and quantitative anatomical descriptions. Combinations of these approaches result in new automatic detection algorithms. Each algorithm exploits anatomical peculiarities of each attachment site, e.g., bone curvature and number of other attachments in a neighborhood of the site. The experimental dataset consisted of 26 anonymized CT sequences containing right and left knee joints in different resolutions. The proposed algorithms take into account bone surface curvatures and spatial differences in locations of medial and lateral parts of both knees. The algorithms for detection of quadriceps femoris, popliteus, biceps femoris tendons, and lateral collateral and medial collateral ligaments attachment sites are provided, as well as examples of their application. Two algorithms are validated by comparison with known statistics of ligaments lengths and also using ground truth annotations for anatomical landmarks approved by clinical experts. The algorithms simplify generation of patient-specific knee joint models demanded in personalized biomechanical models. The algorithms in the current implementation have two important limitations. First, the correctness of the produced results depends on the bones segmentation quality. Second, the presented algorithms detect a point of the attachment site, which is not necessarily its center. Therefore, manual correction of the attachment site location may be required for attachments with relatively large area.

Accurate diagnosis and precise and effective treatment are currently the two magic weapons for dealing with cancer. However, a single marker is often associated with multiple cellular events, which is not conducive to accurate diagnosis, and overly mild treatment methods often make the treatment effect unsatisfactory. In this paper, we construct a Au/Pd octopus nanoparticle-DNA nanomachine (Au/Pd ONP-DNA nanomachine) as a fully automatic diagnosis and treatment logic system. In this system, multiple DNA components are targeting detection units, Au/Pd ONPs act as carriers, and Au/Pd ONPs with an 808 nm laser is the treatment unit. In order to achieve the purpose of precise treatment, we will detect two secondary markers under the premise of detecting one major tumor marker. When all of the designated targets are detected (the logic system input is (1, 1, 1), and the output is (1, 1)), the 808 nm laser can be programmed to automatically radiate tumors and perform photothermal therapy and photodynamic therapy. In vivo and in vitro experiments show that this logic system not only can accurately identify tumor cells but also has considerable therapeutic effects.

One of the most significant pandemics has been raised in the form of Coronavirus disease 2019 (COVID 19). Many researchers have faced various types of challenges for finding the accurate model, which can automatically detect the COVID-19 using computed pulmonary tomography (CT) scans of the chest. This paper has also focused on the same area, and a fully automatic model has been developed, which can predict the COVID-19 using the chest CT scans. The performance of the proposed method has been evaluated by classifying the CT scans of community-acquired pneumonia (CAP) and other non-pneumonia. The proposed deep learning model is based on ResNet 50, named CORNet for the detection of COVID-19, and also performed the retrospective and multicenter analysis for the extraction of visual characteristics from volumetric chest CT scans during COVID-19 detection. Between August 2016 and May 2020, the datasets were obtained from six hospitals. Results are evaluated on the image dataset consisting of a total of 10,052 CT scan images generated from 7850 patients, and the average age of the patients was 50 years. The implemented model has achieved the sensitivity and specificity of 90% and 96%, per scanned image with an AUC of 0. 95.

Surface waves can be used to enhance the characterization of the shallow subsurface in desert environments. A high-resolution shear-wave velocity model is typically obtained by inverting dispersion curves, which correspond to different propagation modes of the surface waves. A common approach to estimate the dispersion curves is to manually pick the magnitude maxima from the frequency-phase velocity spectra of the seismic data. This approach is inefficient, time consuming, highly subjective, and not feasible for large surveys. Automatic picking of dispersion curves has become a topic of interest recently in the oil and gas research community, where many of the developed algorithms were inherited from the fields of image processing and machine learning. By exploring in the area of unsupervised learning, we recently derived an algorithm and workflow for fully automatic picking of surface-wave dispersion curves by employing a density-based spatial clustering technique. Our approach has been tested on the SEG Advanced Modeling Corporation Arid model synthetic data set and a field data set acquired in a desert environment. The results of the synthetic tests show that the estimated dispersion curves match the true dispersion curves with high accuracy, and they can be inverted for shear-wave velocities, successfully recovering the shallow near-surface features. The application of the method to field data provides high-resolution geology-consistent shear-wave velocity information that can be converted into a compressional-wave velocity model in agreement with uphole observations.

BACKGROUND: Convolutional neural networks (CNNs) have the potential to automatically delineate primary nasopharyngeal carcinoma (NPC) on magnetic resonance imaging (MRI), but currently, the literature lacks a module to introduce valuable pre-computed features into a CNN. In addition, most CNNs for primary NPC delineation have focused on contrast-enhanced MRI. To enable the use of CNNs in clinical applications where it would be desirable to avoid contrast agents, such as cancer screening or intra-treatment monitoring, we aim to develop a CNN algorithm with a positional-textural fully-connected attention (FCA) module that can automatically delineate primary NPCs on contrast-free MRI. METHODS: This retrospective study was performed in 404 patients with NPC who had undergone staging MRI. A proposed CNN algorithm incorporated with our positional-textural FCA module (A(proposed)) was trained on manually delineated tumours (M(1st)) to automatically delineate primary NPCs on non-contrast-enhanced T2-weighted fat-suppressed (NE-T2W-FS) images. The performance of A(proposed), three well-established CNNs, Unet (A(unet)), Attention-Unet (A(att)) and Dense-Unet (A(dense)), and a second manual delineation repeated to evaluate human variability (M(2)(nd)) were measured by comparing to the reference standard M(1)(st) to obtain the Dice similarity coefficient (DSC) and average surface distance (ASD). The Wilcoxon rank test was used to compare the performance of A(proposed) against A(unet), A(att), A(dense) and M(2)(nd). RESULTS: A(proposed) showed a median DSC of 0.79 (0.10) and ASD of 0.66 (0.84) mm. It performed better than the well-established networks A(unet) [DSC =0.75 (0.12) and ASD =1.22 (1.73) mm], A(att) [DSC =0.75 (0.10) and ASD =0.96 (1.16) mm] and A(dense) [DSC =0.71 (0.14) and ASD =1.67 (1.92) mm] (all P

Hydrometers are widely used in industry for liquid density measurement. It is important to achieve rapid and high accuracy calibration for hydrometers. Based on the Archimedes principle, a fully automatic hydrometer calibration system in NIM was designed using Cuckow’s method. The liquid density of n-tridecane (C13H28)is calibrated with 441 g high-purity fused silica ring as the solid density standard. The buoyancy of hydrometer is measured by static weighing system with resolution 0.01 mg. The alignment between liquid surface and hydrometer scale was achieved by the lifting platform with the positioning accuracy of 10 μm. According to the weighing value of hydrometer in air and liquid, the density correction value at different scales is calculated. Hydrometer covering a full range (650–1500) kg/m3can be calibrated without changing the liquid. Taking the calibration data of PTB as reference, the experimental data show that the measurement uncertainty of this system is better than 0.3 division (k = 2).

In this article, we propose a fully automatic system for generating comic books from videos without any human intervention. Given an input video along with its subtitles, our approach first extracts informative keyframes by analyzing the subtitles and stylizes keyframes into comic-style images. Then, we propose a novel automatic multi-page layout framework that can allocate the images across multiple pages and synthesize visually interesting layouts based on the rich semantics of the images (e.g., importance and inter-image relation). Finally, as opposed to using the same type of balloon as in previous works, we propose an emotion-aware balloon generation method to create different types of word balloons by analyzing the emotion of subtitles and audio. Our method is able to vary balloon shapes and word sizes in balloons in response to different emotions, leading to more enriched reading experience. Once the balloons are generated, they are placed adjacent to their corresponding speakers via speaker detection. Our results show that our method, without requiring any user inputs, can generate high-quality comic pages with visually rich layouts and balloons. Our user studies also demonstrate that users prefer our generated results over those by state-of-the-art comic generation systems.

Numerous methods have been published to segment the infarct tissue in the left ventricle, most of them either need manual work, post-processing, or suffer from poor reproducibility. We proposed an automatic segmentation method for segmenting the infarct tissue in left ventricle with myocardial infarction. Cardiac images of a total of 60 diseased hearts (55 human hearts and 5 porcine hearts) were used in this study. The epicardial and endocardial boundaries of the ventricles in every 2D slice of the cardiac magnetic resonance with late gadolinium enhancement images were manually segmented. The subsequent pipeline of infarct tissue segmentation is fully automatic. The segmentation results with the automatic algorithm proposed in this paper were compared to the consensus ground truth. The median of Dice overlap between our automatic method and the consensus ground truth is 0.79. We also compared the automatic method with the consensus ground truth using different image sources from different centers with different scan parameters and different scan machines. The results showed that the Dice overlap with the public dataset was 0.83, and the overall Dice overlap was 0.79. The results show that our method is robust with respect to different MRI image sources, which were scanned by different centers with different image collection parameters. The segmentation accuracy we obtained is comparable to or better than that of the conventional semi-automatic methods. Our segmentation method may be useful for processing large amount of dataset in clinic.

Early detection of all types of lung nodules with different characters in medical modality images using computer-aided detection is the best acceptable remedy to save the lives of lung cancer sufferers. But accuracy of different types of nodule detection rates is based on chosen segmented procedures for parenchyma and nodules. Separation of pleural from juxta-pleural nodules (JPNs) is difficult as intensity of pleural and attached nodule is similar. This research paper proposes a fully automated method to detect and segment JPNs. In the proposed method, lung parenchyma is segmented using iterative thresholding algorithm. To improve the nodules detection rate separation of connected lung lobes, an algorithm is proposed to separate connected left and right lung lobes. The new method segments JPNs based on lung boundary pixels extraction, concave points extraction, and separation of attached pleural from nodule. Validation of the proposed method was performed on LIDC-CT images. The experimental result confirms that the developed method segments the JPNs with less computational time and high accuracy.

PURPOSE Cardiac relaxometry techniques, particularly T1 mapping, have recently gained clinical importance in various cardiac pathologies. Myocardial T1 and extracellular volume are usually calculated from manual identification of left ventricular epicardial and endocardial regions. This is a laborious process, particularly for large volume studies. Here we present a fully automated relaxometry framework (FASTR) for segmental analysis of T1 maps (both native and postcontrast) and partition coefficient (λ). METHODS Patients (N = 11) were imaged postacute myocardial infarction on a 1.5T clinical scanner. The scan protocol involved CINE-SSFP imaging, native, and post-contrast T1 mapping using the Modified Look-Locker Inversion (MOLLI) recovery sequence. FASTR consisted of automatic myocardial segmentation of spatio-temporally coregistered CINE images as an initial guess, followed by refinement of the contours on the T1 maps to derive segmental T1 and λ. T1 and λ were then compared to those obtained from two trained expert observers. RESULTS Robust endocardial and epicardial contours were achieved on T1 maps despite the presence of infarcted tissue. Relative to experts, FASTR resulted in myocardial Dice coefficients (native T1: 0.752 ± 0.041; postcontrast T1: 0.751 ± 0.057) that were comparable to interobserver Dice (native T1: 0.803 ± 0.045; postcontrast T1: 0.799 ± 0.054). There were strong correlations observed for T1 and λ derived from experts and FASTR (native T1: r = 0.83; postcontrast T1: r = 0.87; λ: r = 0.78; P

For three-weekly hemodialysis, a single-pool Kt/V target of at least 1.4 together with a minimal dialysis dose Kt at 45 L for men and 40 L for women per each session is currently recommended. Fully automatic online calculation of Kt and Kt/V from conductivity or UV-absorbance measurements in the dialysate is standardly implemented on some hemodialysis monitors and makes it possible to estimate the dialysis dose without the need for blood or dialysate samples. Monitoring the UV-absorbance of the spent dialysate is the most direct method for estimating Kt/V as it does not require an estimate of V. Calculation of ionic dialysance from conductivity measurements is the most direct method for estimating Kt and BSA-scaled dialysis dose. Both ionic dialysance monitoring and UV-absorbance monitoring may help detect a change in urea clearance occurring during the session, but this change must be interpreted differently depending on the monitoring being considered. An abrupt decrease in urea clearance results in a decrease in ionic dialysance but, paradoxically, a sudden increase in estimated urea clearance provided by dialysate UV-absorbance monitoring. Healthcare teams who monitor both ionic dialysance and UV-absorbance in their hemodialysis units must be clearly informed of this difficulty.

The prevalence of carbapenemase-producing organisms (CPOs) globally poses a public health threat; however, detecting carbapenemases is a challenge because of their variety. GENECUBE, a fully automated gene analyzer, detects a target gene in a short time and simultaneously detects its single nucleotide polymorphism. We used this property to develop for the first time a rapid assay for detecting CPOs from cultured bacteria using GENECUBE. The original primer-probe sets were used to detect blaKPC, blaIMP, blaVIM, blaNDM, and blaOXA-48-like from 149 CPOs (nine types) and 61 non-CPOs. The sensitivity, specificity, and positive and negative predictions of the GENECUBE assay were 100%. This assay detected carbapenemase single-producers and carbapenemase co-producers with 100% accuracy. The time required for detects of four types of carbapenemase at one run was about 30 min, but it took about 1 h to detect all five types. In addition, this assay performed the rapid detection and classification of blaOXA-48, blaOXA-181, blaOXA-232, and blaOXA-244 simultaneously. The GENECUBE assay is a promising tool for controlling the spread of CPOs and helping to select accurate and rapid antibiotic therapies.

A large number of outstanding heritage sites and artifacts around the world are fragile assets, faced with different and continuous challenges. The loss of archaeological heritage indeed continues today at a constant pace, both due to natural disasters and human-made actions. The magnitude of the problem requires effective measures that can support the restoration activities in case of disasters. The latter, indeed, can be difficult and time-consuming, especially when dealing with the recomposition of fragmented and scattered objects. This is often a manual procedure that does not always lead to the correct identification of the numerous possible matches. The heritage science community urges for methods able to optimize the research efforts and the fragments matching reliability and speed. The proposed workflow (FRAGMENTS) introduces a fully automatic approach based on photogrammetric techniques, given a pre-disaster image is available.

Highlights • Fully automatic estimation of the peak frequency of the posterior dominant rhythm. • Automatic estimates are very similar to human ratings. • Algorithm made available with a simple graphical interface., Objective To develop and test a fully automated method for estimation of the peak frequency of the posterior dominant rhythm (PDR) in a large retrospective EEG cohort. Methods Thresholding was used to select suitable EEG data segments for spectral estimation for electrode O1 and O2. A random sample of 100 peak frequency estimates were blindly rated by two independent raters to validate the results of the automatic PDR peak frequency estimates. We investigated the relationship with age, sex and binary EEG classification. Results There were 9197 eligible EEGs which resulted in a total of 6104 PDR peak frequency estimates. The relationship between automatic estimates and age was found to be consistent with the literature. The correlation between human ratings and automatic scoring was very high, rho = 0.94–0.95. There was a sex difference of d = 0.33 emerging at puberty with females having a faster PDR peak frequency than males. Conclusions Fully automatic PDR peak frequency estimation not dependent on annotated EEG produced results that are very close to human ratings. Significance PDR peak frequency can be automatically estimated. A compiled version of the algorithm is included as an app for independent use.

Our objective is the calibration of mathematical tumor growth models from a single multiparametric scan. The target problem is the analysis of preoperative Glioblastoma (GBM) scans. To this end, we present a fully automatic tumor-growth calibration methodology that integrates a single-species reaction-diffusion partial differential equation (PDE) model for tumor progression with multiparametric Magnetic Resonance Imaging (mpMRI) scans to robustly extract patient specific biomarkers i.e., estimates for ( i ) the tumor cell proliferation rate , ( ii ) the tumor cell migration rate , and ( iii ) the original, localized site(s) of tumor initiation . Our method is based on a sparse reconstruction algorithm for the tumor initial location (TIL). This problem is particularly challenging due to nonlinearity, ill-posedeness, and ill conditioning. We propose a coarse-to-fine multi-resolution continuation scheme with parameter decomposition to stabilize the inversion. We demonstrate robustness and practicality of our method by applying the proposed method to clinical data of 206 GBM patients. We analyze the extracted biomarkers and relate tumor origin with patient overall survival by mapping the former into a common atlas space. We present preliminary results that suggest improved accuracy for prediction of patient overall survival when a set of imaging features is augmented with estimated biophysical parameters. All extracted features, tumor initial positions, and biophysical growth parameters are made publicly available for further analysis. To our knowledge, this is the first fully automatic scheme that can handle multifocal tumors and can localize the TIL to a few millimeters.

Since microbial detection is an important aspect for the prevention and control of foodborne diseases, an ideal detection system with high sensitivity, strong specificity, and timeliness is needed. Here, we proposed a fluorescent and opt-electric recording bacterial identification device (FORBID) for fully automatic real-time photoelectric sensing analysis of microbials by integrating the metabolic characteristics of microbial and selective substrate catalysis. It simplifies the testing process (one-step) and decreases the need of professional technicians. Besides, the system exhibits ultrasensitive (1 CFU/mL) and specific detection (99%) in both microbials, Escherichia coli and Pseudomonas aeruginosa. More importantly, the timeliness of this system is even better than that of the traditional culture methods. It is believed that this system can be extended to the detection of other microorganisms and provide a potential alternative for the detection of pathogens.

The article considers the issues of monitoring the thermal conditions of alloys melting and casting at foundries. It is noted that the least reliable method is when the measurement and fixing the temperature is assigned to the worker. On the other hand, a fully automatic approach is not always available for small foundries. In this regard, the expediency of using an automated approach is shown, in which the measurement is assigned to the worker, and the values are recorded automatically. This method assumes implementation of an algorithm for automatic classification of temperature measurements based on an end-to-end array of data obtained in the production stream. The solving of this task is divided into three stages. Preparing of raw data for classification process is provided on the first stage. On the second stage, the task of measurement classification is solved by using neural network principles. Analysis of the results of the artificial neural network has shown its high efficiency and degree of their correspondence with the actual situation on the work site. It was also noted that the application of artificial neural networks principles makes the classification process flexible, due to the ability to easily supplement the process with new parameters and neurons. The final stage is analysis of the obtained results. Correctly performed data classification provides an opportunity not only to assess compliance with technological discipline at the site, but also to improve the process of identifying the causes of casting defects. Application of the proposed approach allows us to reduce the influence of human factor in the analysis of thermal conditions of alloys melting and casting with minimal costs for melting monitoring.

Sensors are the primary component of a monitoring and control system. Effective monitoring and control of the microclimatic environment in a greenhouse is the key necessity for protecting crops from adverse environments. Moreover,the greenhouse microclimate is influenced by various factors. In the large-scale greenhouse facilities, several sensors and actuators are needed to control the system. Manual monitoring and control of such a large and complex system is labor-intensive and impractical. Therefore, an automatic monitoring and control system in the greenhouse becomes indispensable. In addition, microclimatic parameters such as temperature, humidity, and solar irradiance in the greenhouse are non-linearly interlinked, thereby forming a non-linear multivariate system. Thus, an appropriately designed sensor system is needed for monitoring and controlling the greenhouse microclimate. Research articles on greenhouse microclimate monitoring and control published in the last 6 years were considered. The sensor devices and technologies applied to control particular environmental parameters in the greenhouse and their key achievements were systematically reviewed. In addition, different approaches to determine the optimum number of sensors and their placement inside the greenhouse were investigated. It was found that spatially installed sensor devices above the plant height reflect the actual information of the environment getting by plant. Furthermore, both hardware and software-based sensing techniques control the greenhouse microclimate optimally. The proper positioning of sensors and their protection from harsh environmental factors is also essential. It can be concluded that modern sensor devices and systems are driving the greenhouse monitoring and control system toward an intelligent, real-time, remotely accessible, and fully automatic system.

Automation in software testing is significantly growing in recent situation. Most part of the system is automated with help of the software. Today every modern software developers are trying to automate the software development process as much as possible. Therefore to develop any software more skills and expertise are needed. For the software development process, testing of software is the exceedingly significant and considerable phase. Automatic test data generation had an essential function in specific area regarding software testing. Test case creation is technique of gathering the data which completes the testing standards, all criteria’s and conditions. During testing process, the software goes through frequent modifications. As a result, due to all of these modifications and repetitive retesting the cost of testing process increases. This is called regression testing. Regression testing requires more expertise, more effort, more time and more cost. Here to reduce the time and expenditure, many type of techniques are proposed. The changes in one test case will affect all the others test cases. To triumph over this problem, when the changes occurred in the software every the test case have to be retested repeatedly. And this problem leads to make the testing process time consuming with unnecessary increased cost. Here In this research paper, the work’s focal point on automatic test cases generation and prioritization with improved evolutionary genetic algorithm.

This paper presents an adaptive strategy for phase-field simulations with transition to fracture. The phase-field equations are solved only in small subdomains around crack tips to determine propagation, while an XFEM discretization is used in the rest of the domain to represent sharp cracks, enabling to use a coarser discretization and therefore reducing the computational cost. Crack-tip subdomains move as cracks propagate in a fully automatic process. The same computational mesh is used during all the simulation, with an $h$-refined approximation in the elements in the crack-tip subdomains. Continuity of the displacement between the refined subdomains and the XFEM region is imposed in weak form via Nitsche's method. The robustness of the strategy is shown for some numerical examples in 2D and 3D, including branching and coalescence tests., v2: corrected typos and references

Budgeting and planning processes require medium-term sales forecasts with marketing scenarios. The complexity in modern retailing necessitates consistent, automatic forecasting and insight generation. Remedies to the high dimensionality problem have drawbacks; black box machine learning methods require voluminous data and lack insights, while regularization may bias causal estimates in interpretable models. The proposed FAIR (Fully Automatic Interpretable Retail forecasting) method supports the retail planning process with multi-step-ahead category-store level forecasts, scenario evaluations, and insights. It considers category-store specific seasonality, focal- and cross-category marketing, and adaptive base sales while dealing with regularization-induced confounding. We show, with three chains from the IRI dataset involving 30 categories, that regularization-induced confounding decreases forecast accuracy. By including focal- and cross-category marketing, as well as random disturbances, forecast accuracy is increased. FAIR is more accurate than the black box machine learning method Boosted Trees and other benchmarks while also providing insights that are in line with the marketing literature., NA

The intima media thickness (IMT) of the common carotid artery (CCA) can be used to predict the risk of atherosclerosis. Many image segmentation techniques have been used for IMT measurement. However, severe noise in the ultrasound image can lead to erroneous segmentation results. To improve the robustness to noise, a fully automatic method, based on an improved Otsu’s method and an adaptive wind-driven optimization technique, is proposed for estimating the IMT (denoted as “improved Otsu-AWDO”). First, an advanced despeckling filter, i.e., “ Nagare’s filter” is used to address the speckle noise in the carotid ultrasound images. Next, an improved fuzzy contrast method (IFC) is used to enhance the region of the intima media complex (IMC) in the blurred filtered images. Then, a new method is used for automatic extraction of the region of interest (ROI). Finally, the lumen intima interface and media adventitia interface are segmented from the IMC using improved Otsu-AWDO. Then, 156 B-mode longitudinal carotid ultrasound images of six different datasets are used to evaluate the performance of the automatic measurements. The results indicate that the absolute error of proposed method is only 10.1 ± 9.6 (mean ± std in μm). Moreover, the proposed method has a correlation coefficient as high as 0.9922, and a bias as low as 0.0007. From comparison with previous methods, we can conclude that the proposed method has strong robustness and can provide accurate IMT estimations.

The presented manuscript proposes a fully automatic deep learning method to quantify the tumour region in brain Magnetic Resonance images as the accurate diagnosis of brain tumour region is necessa...

OBJECTIVE: To compare the accuracy of cephalometric analyses made with fully automated tracings, computerized tracing, and app-aided tracings with equivalent hand-traced measurements, and to evaluate the tracing time for each cephalometric analysis method. METHODS: Pre-treatment lateral cephalometric radiographs of 40 patients were randomly selected. Eight angular and 4 linear parameters were measured by 1 operator using 3 methods: computerized tracing with software Dolphin Imaging 13.01(Dolphin Imaging and Management Solutions, Chatsworth, Calif, USA), app-aided tracing using the CephNinja 3.51 app (Cyncronus LLC, WA, USA), and web-based fully automated tracing with CephX (ORCA Dental AI, Las Vegas, NV). Correction of CephX landmarks was also made. Manual tracings were performed by 3 operators. Remeasurement of 15 radiographs was carried out to determine the intra-examiner and inter-examiner (manual tracings) correlation coefficient (ICC). Inter-group comparisons were made with one-way analysis of variance. The Tukey test was used for post hoc testing. RESULTS: Overall, greater variability was found with CephX compared with the other methods. Differences in GoGn-SN (°), I-NA (°), I-NB (°), I-NA (mm), and I-NB (mm) were statistically (p

Speckle tracking echocardiography (STE) enables quantification of myocardial deformation by a generation of spatiotemporal strain curves or time-strain curves (TSCs). Currently, only assessment of peak global longitudinal strain is employed in clinical practice because of the uncertainty in the accuracy of STE. We describe a supervised machine learning, physiologically constrained, fully automatic algorithm, trained with labeled data, for classification of TSCs into physiologic or artifactual classes. The data set of 415 healthy patients, with three cine loops per patient, corresponding to the three standard 2-D longitudinal views, was processed using a previously published, in-house STE software termed K-SAD. We report an accuracy of 86.4% for classifying TSCs as physiologic, artifactual and undetermined curves. The positive predictive value for a physiologic strain curve is 89%. This is as a necessary step for a similar separation of pathologic conditions, to allow full utilization of the temporal information concealed in layer-specific segmental TSCs.

[EN] Image-based and range-based solutions can be used for the acquisition of valuable data in medicine. However, most of these methods are not valid for non-static patients. Cranial deformation is a problem with high prevalence among infants and image-based solutions can be used to assess the degree of deformation and monitor the evolution of patients. However, it is required to deal with infants normal movement during the assessment in order to avoid sedation. Some high-end multiple-sensor image-based solutions allow the achievement of accurate 3D data for medical applications under unpredicted dynamic conditions in consultation. In this paper, a novel, single photogrammetric smartphone-based solution for cranial deformation assessment is presented. A coded cap is placed on the infant's head and a guided smartphone app is used by the user to acquire the information, that is later processed on a server to obtain the 3D model. The smartphone app is designed to guide users with no knowledge of photogrammetry, computer vision or 3D modelling. The processing is fully automatic offline. The photogrammetric tool is also non-invasive, reacting well with quick and sudden infant's movements. Therefore, it does not require sedation. This paper tackles the accuracy and repeatability analysis tested both for a single user (intrauser) and multiple non-expert user (interuser) on 3D printed head models. The results allow us to confirm an accuracy below 1.5 mm, which makes the system suitable for clinical practice by medical staff. The basic automatically-derived anthropometric linear magnitudes are also tested obtaining a mean variability of 0.6 +/- 0.6 mm for the longitudinal and transversal distances and 1.4 +/- 1.3 mm for the maximum perimeter., This project is funded by Instituto de Salud Carlos III and European Regional Development Fund (FEDER), project number PI18/00881, and by Generalitat Valenciana, grant number ACIF/2017/056.

Purpose Precise segmentation of brain lesions is essential for neurological research. Specifically, resection volume estimates can aid in the assessment of residual postoperative tissue, e.g. following surgery for glioma. Furthermore, behavioral lesion-symptom mapping in epilepsy relies on accurate delineation of surgical lesions. We sought to determine whether semi- and fully automatic segmentation methods can be applied to resected brain areas and which approach provides the most accurate and cost-efficient results. Methods We compared a semi-automatic (ITK-SNAP) with a fully automatic (lesion_GNB) method for segmentation of resected brain areas in terms of accuracy with manual segmentation serving as reference. Additionally, we evaluated processing times of all three methods. We used T1w, MRI-data of epilepsy patients (n = 27; 11 m; mean age 39 years, range 16–69) who underwent temporal lobe resections (17 left). Results The semi-automatic approach yielded superior accuracy (p p > 0.05). Manual segmentation required more human input (30.41 min/subject) and therefore inferring significantly higher costs than semi- (3.27 min/subject) or fully automatic approaches (labor and cost approaching zero). Conclusion Semi-automatic segmentation offers the most accurate results in resected brain areas with a moderate amount of human input, thus representing a viable alternative compared with manual segmentation, especially for studies with large patient cohorts.

In this paper, we present a fully automatic evaluation approach that can be used for fast inline CT scanning. In contrast to classical defect-recognition algorithms, this approach does not require ...

Purpose Rheumatoid arthritis (RA) causes joint space narrowing (JSN) as a form of joint destruction. We developed an automatic system that can detect joint locations and compute the joint space difference index (JSDI), which was defined as the chronological change in JSN between two radiographs. This study aims to evaluate the application of "machine vision" for radiographic image of the finger joints. Materials and methods Fifteen RA patients with long-term sustained clinical low disease activity were recruited. All patients underwent hand radiography and power Doppler ultrasonography (PDUS). The JSN was evaluated using the Genant-modified Sharp scoring (GSS) method and the automatic system. Synovial vascularity (SV) was assessed quantitatively using ultrasonography. Results There were no significant differences in the JSDI between the joints with JSN and those without JSN on GSS (p = 0.052). The JSDI of the joints with SV was significantly higher than those without SV (p = 0.043). The JSDI of the no therapeutic response group was significantly higher than those of the response group (p < 0.001). Conclusion Our software can automatically evaluate temporal changes of JSN, which might free rheumatologists / radiologists from the burden of scoring hand radiography.

The mapping of water bodies is an important application area of satellite-based remote sensing. In this contribution, a simple framework based on supervised learning and automatic training data annotation is shown, which allows to map inland water bodies from Sentinel satellite data on large scale, i.e. on state level. Using the German state of Bavaria as an example and different combinations of Sentinel-1 SAR and Sentinel-2 multi-spectral imagery as inputs, potentials and limits for the automatic detection of water surfaces for rivers, lakes, and reservoirs are investigated. Both quantitative and qualitative results confirm that fully automatic large-scale inland water body mapping is generally possible from Sentinel data; whereas, the best result is achieved when all available surface-related bands of both Sentinel-1 and Sentinel-2 are fused on a pixel level. The main limitation arises from missed smaller water bodies, which are not observed in bands with a resolution of about 20 m. Given the simplicity of the proposed approach and the open availability of the Sentinel data, the study confirms the potential for a fully automatic large-scale mapping of inland water with cloud-based remote sensing techniques.

The constantly increasing number of computed tomography (CT) examinations poses major challenges for radiologists. In this article, the additional benefits and potential of an artificial intelligence (AI) analysis platform for chest CT examinations in routine clinical practice will be examined. Specific application examples include AI-based, fully automatic lung segmentation with emphysema quantification, aortic measurements, detection of pulmonary nodules, and bone mineral density measurement. This contribution aims to appraise this AI-based application for value-added diagnosis during routine chest CT examinations and explore future development perspectives.

International audience; We present Lexifield, a fully automatic language-independent system for building domain-specific lexicons from a short list of terms defining the domain. Lexifield relies on a pre-trained word embedding model, a definition dictionary and a dictionary of synonyms. To evaluate this system, four lexicons have been generated: one lexicon in French for the topic “son” (“sound”) and three lexicons in English for the topics “sound”, “taste” and “odour”. As compared to other word embedding-based systems and a state-of-the-art sensorial lexicon, Sensicon, our system achieves better precision and recall on reference lists extracted from manually created resources such as Roget’s Thesaurus.

Fenjiu spirits are famous for their multifarious flavors in China. Nevertheless, the uniformity and homogeneity of the products are always challenges for the producers. Three flavor measurement methods, that is, zNose™, GC‐MS, and spirit tasters, were employed and correlated for evaluation of Fenjiu flavors' difference. An automatic blending system was designed to improve the uniformity, where base liquors of Dazongjiu, Dajiu, and Daijiu, as well as pure water, were used for blending, and BPNN was employed for regression and error minimization. Results showed that zNose™ results could be correlated with GC‐MS in 95%; hence, zNose™ can replace GC‐MS in Fenjiu flavor measurement. With the zNose™ assistance, an experimental scale, fully automatic blending system could be optimized with different modern mathematical algorithms to improve the Fenjiu products uniformity. We believe that this detailed work will advance the scientific knowledge in the field and help to facilitate the uniformity and homogeneity of Fenjiu products., Three flavor measurement methods, that is, GC‐MS, liquor tasters, and zNose, were employed for evaluation of flavors difference between the same Chinese spirit products. zNose was correlated with GC‐MS in 95% and could be a replacement of GC‐MS in Fenjiu flavor measurement. An automatic blending system with the zNose assistance was designed to improve the Fenjiu products uniformity.

The aim of this paper is to present the research book “The Grobner Cover” Montes (The Grobner Cover, ACM-series, Springer, Berlin, 2019) recently published. This book is divided into two parts, one theoretical and one focusing on applications, and offers a complete description of the Canonical Grobner Cover, to the author’s best knowledge, the most accurate algebraic method for discussing parametric polynomial systems. It also includes applications to the automatic deduction of geometric theorems, loci computation and envelopes. The theoretical part is a self-contained exposition on the theory of Parametric Grobner Systems and Bases. It begins with Weispfenning introduction of Comprehensive Grobner Systems (CGS), a fundamental contribution made in 1992, and provides a complete description of the Canonical Grobner Cover (GC), which includes a canonical discussion of a set of parametric polynomial equations developed in Montes, Wibmer (J Symb Comput 45:1391–1425, 2010). In turn, the application part selects three problems for which the Grobner Cover offers valuable new perspectives. The automatic deduction of geometric theorems (ADGT) becomes fully automatic and straightforward using GC, representing a major improvement on all previous methods. In terms of loci and envelope computation, GC makes it possible to introduce a taxonomy of the components and automatically compute it. The book also generalizes the definition of the envelope of a family of hyper-surfaces, and provides algorithms for its computation, as well as for discussing how to determine the real envelope. All the algorithms described in the book have also been included in the Singular software library grobcov.lib implemented by the author and H. Schonemann, the book serving also as User Manual for the library.

Duplicate detection is an integral part of data cleaning and serves to identify multiple representations of same real-world entities in (relational) datasets. Existing duplicate detection approaches are effective, but they are also hard to parameterize or require a lot of pre-labeled training data. Both parameterization and pre-labeling are at least domain-specific if not dataset-specific, which is a problem if a new dataset needs to be cleaned. For this reason, we propose a novel, rule-based and fully automatic duplicate detection approach that is based on matching dependencies (MDs). Our system uses automatically discovered MDs, various dataset features, and known gold standards to train a model that selects MDs as duplicate detection rules. Once trained, the model can select useful MDs for duplicate detection on any new dataset. To increase the generally low recall of MD-based data cleaning approaches, we propose an additional boosting step. Our experiments show that this approach reaches up to 94% F-measure and 100% precision on our evaluation datasets, which are good numbers considering that the system does not require domain or target data-specific configuration.

Measurement of the mechanical dyssynchrony of the ventricle is important for the cardiac resynchronization therapy (CRT) and the evaluation of patients with heart failures. So far, feature-tracking cardiovascular magnetic resonance (FT-CMR) has become the most popular method for measuring the mechanical dyssynchrony. FT-CMR calculates the strain values based on the pre-defined segments of the ventricle and thus it might miss some important regional information. In this paper, we propose a new approach to measure the intra-ventricular mechanical dyssynchrony based on the uniformly sampled points from all the regions of the ventricle. Accordingly, the proposed approach will not miss any regional information of the ventricle. In addition, the proposed approach is fully automatic and not depedent on the training sets. It calculates the mechnical dyssynchrony rapidly and determines whether the tested case is normal or patient immdiately. The proposed approach was validated with 40 tested cases (20 normal cases and 20 patient cases). Experimental results showed that the fully automatic intra-ventricular mechanical dyssynchrony measurement approach achieved 100% diagnosis accuracy for the left ventricle (LV) and achieved 95% diagnoisis accuracy for the right ventricle (RV).

In this paper, we report a fully automatic method for the prediction of the treatment efficacy of photodynamic therapy during the clinical treatment in port-wine stains. Histogram of oriented gradients (HOG) features were extracted from optical coherence tomography images. Isolation forest (iForest) was used to build classifier based on these features, achieving a sensitivity of 84% and specificity of 91%. Our dataset consists of 336 PWS lesions of 121 patients. We aim to build a comprehensive computational model for the patients who respond positively to the photodynamic therapy, which could be used to sort and identify patients who respond poorly to photodynamic therapy before treatment and prevent them from unnecessary treatment.

In the following paper, we investigated the usefulness of future reference sentence patterns in the prediction of the unfolding of future events. To obtain such patterns we first collected sentences that have any reference to the future from newspapers and Web news. Based on this collection, we developed a novel method for automatic extraction of frequent patterns from such sentences. The extracted patterns, consisting of multilayer semantic information and morphological information, were implemented in the formation of a general model of linguistically expressed future. To fully assess the performance of the proposed method we performed a number of evaluation experiments. In the first experiment, we evaluated the automatic extraction of future reference sentence patterns with the proposed extraction algorithm. In the second set of experiments, we estimated the effectiveness of those patterns and applied them to automatically classify sentences into future referring and other. The final model was then tested for performance in retrieving a new set of future reference sentences from a large news corpus. The obtained results confirmed that the proposed method outperformed state-of-the-art method in fully automatic retrieval of future reference sentences. Lastly, we applied the method in practice to confirm its usefulness in two tasks. The first is to support human readers in the everyday prediction of unfolding future events. In the second task, we developed a fully automatic prototype method for future prediction and tested its performance using the tasks included in the official Future Prediction Competence Test. The results indicate that the prototype system outperforms natural human foreseeing capability.

Background Microalgae are emerging as promising sustainable sources for biofuels, biostimulants in agriculture, soil bioremediation, feed and human nutrients. Nonetheless, the molecular mechanisms underpinning microalgae physiology and the biosynthesis of compounds of biotechnological interest are largely uncharacterized. This hinders the development of microalgae full potential as cell-factories. The recent application of omics technologies into microalgae research aims at unraveling these systems. Nevertheless, the lack of specific tools for analysing omics raw data generated from microalgae to provide biological meaningful information are hampering the impact of these technologies. The purpose of ALGAEFUN with MARACAS consists in providing researchers in microalgae with an enabling tool that will allow them to exploit transcriptomic and cistromic high-throughput sequencing data. Results ALGAEFUN with MARACAS consists of two different tools. First, MARACAS (MicroAlgae RnA-seq and Chip-seq AnalysiS) implements a fully automatic computational pipeline receiving as input RNA-seq (RNA sequencing) or ChIP-seq (chromatin immunoprecipitation sequencing) raw data from microalgae studies. MARACAS generates sets of differentially expressed genes or lists of genomic loci for RNA-seq and ChIP-seq analysis respectively. Second, ALGAEFUN (microALGAE FUNctional enrichment tool) is a web-based application where gene sets generated from RNA-seq analysis as well as lists of genomic loci from ChIP-seq analysis can be used as input. On the one hand, it can be used to perform Gene Ontology and biological pathways enrichment analysis over gene sets. On the other hand, using the results of ChIP-seq data analysis, it identifies a set of potential target genes and analyses the distribution of the loci over gene features. Graphical representation of the results as well as tables with gene annotations are generated and can be downloaded for further analysis. Conclusions ALGAEFUN with MARACAS provides an integrated environment for the microalgae research community that facilitates the process of obtaining relevant biological information from raw RNA-seq and ChIP-seq data. These applications are designed to assist researchers in the interpretation of gene lists and genomic loci based on functional enrichment analysis. ALGAEFUN with MARACAS is publicly available on https://greennetwork.us.es/AlgaeFUN/.

Rapid screening of foodborne pathogens is key to preventing food poisoning. In this study, a slidable centrifugal disc was developed for automatic and multi-channel detection of Salmonella typhimurium using Flinders Technology Associates (FTA) cards for nucleic acid extraction and recombinase aided amplification (RAA) for nucleic acid detection. The slidable FTA switching and centrifugal fluidic control were elaborately combined to achieve fully automatic operations, including centrifugation of the bacterial sample to obtain the concentrated bacteria, heating and drying of the FTA card to extract the nucleic acids, washing of the FTA card to remove the impurities, and RAA detection of the extracted DNA to determine the concentration. Under the optimal conditions, this slidable centrifugal disc was able to detect 10 CFU mL-1 in a spiked chicken meat supernatant in 1 h with an average recovery of 101.8% and an average standard deviation of 6.5%. This disc has been demonstrated as an alternative for sample-in-result-out detection of Salmonella and has shown potential for simultaneous detection of multiple bacteria.

Computerized ray-tracing has exponentially evolved since its inception. Various software companies have assisted the optical designer in optimization of lens designs. We introduce a fully automatic optimization method reducing optimization time ten-fold.

Microfluidic chip analysis has great potential advantages such as high integration, fast speed analysis, and automatic operation and is widely used not only in biological fields but also in many other analytical areas such as agriculture and food safety. Herein, a fully automatic multi-class multi-residue analysis of veterinary drugs simultaneously in an integrated chip-mass spectrometry (chip-MS) platform was developed. The developed microfluidic chip platform integrated three modules including the extraction and filtration module, "pass-through" clean-up module, and online evaporation module. The resulting chip has been coupled to a MS detector successfully, in which 23 kinds of residues in five classes were simultaneously qualitatively and quantitatively detected without chromatographic separation, obtaining the limits of detection of the spiked milk sample in the range of 0.23-4.13 ng/mL and the recovery rate in the range from 71.7 to 118.0% under optimized conditions. The microfluidic chip system developed in this study provided a new idea for the development of detection chips and exhibited considerable potential in the point-of-care testing in milk.

This article is dedicated to the spatialization of image contents, with a focus on geographical iconographic heritage, i.e. digitized or born-digital image collections, acquired at variable temporal periods and showing the territory and its human-made and natural visual landmarks. We present a panorama of the current solutions (manual, semi-automatic and fully automatic alternatives) that exist to spatialize a visual content, with respect to the data available and the level of spatialization targeted. In particular, we highlight the characteristics of the approaches dedicated to geographical iconographic heritage, and in some cases, we present tests and practical feedbacks that we had the opportunity to conduct for old photographic contents in oblique aerial and terrestrial imagery.

Waste management has become one of our biggest environmental issues, which, in addition to disrupting the environment's balance, has negative consequences for society's health. One of the most pressing issues of our time is the identification, tracking, and management of waste. The traditional method of manually tracking waste in waste dustbins is a complicated and time-consuming process that requires more human effort, time, and money, and is in no way compatible with modern technologies. This is a cutting-edge form of waste management that is fully automatic. This paper actually proposes a system that monitors the level of the Garbage System. The system will assist in keeping the metropolises and environment clean. This proposed system tracks the garbage bins and provides information about the amount of garbage collected through a web page. (Node.js programming was used for the back-end development)