Quality Control Algorithm Research Articles

Technical note: A guide to using three open-source quality control algorithms for rainfall data from personal weather stations

Abstract. The number of rainfall observations from personal weather stations (PWSs) has increased significantly over the past years; however, there are persistent questions about data quality. In this paper, we reflect on three quality control algorithms (PWSQC, PWS-pyQC, and GSDR-QC) designed for the quality control (QC) of rainfall data. Technical and operational guidelines are provided to help interested users in finding the most appropriate QC to apply for their use case. All three algorithms can be accessed within the OpenSense sandbox where users can run the code. The results show that all three algorithms improve PWS data quality when cross-referenced against a rain radar data product. The considered algorithms have different strengths and weaknesses depending on the PWS and official data availability, making it inadvisable to recommend one over another without carefully considering the specific setting. The authors highlight a need for further objective quantitative benchmarking of QC algorithms. This requires freely available test datasets representing a range of environments, gauge densities, and weather patterns.

Informatization of the processes of administration of the quality of medical services in surgical hospitals

Among the various forms of quality control of the provision of medical care, self-control and internal control of the quality of medical services are the most effective and significant. Currently, there is no comprehensive information technology and algorithm for recording data on the provision of medical services and determining their quality. The goal is to develop and analyze the effectiveness of business processes and an algorithm for quality control of the performance of medical services in a surgical health care institution by establishing their daily accounting in the information system, control of indicators characterizing the safety and quality of their performance, as well as an opportunity to assess the dynamics changes in indicators for making management decisions. In the study, an analysis of literary sources was carried out, business processes were formulated, and on the materials of a retrospective analysis algorithms for the use of information technologies were developed based on the methods of generalization, comparison, content analysis, system-analytical and logical analysis, a graphic analysis was performed to increase the clarity of the presentation of the material. The research was approved by the Biomedical Ethics Committee. For the first time, as a result of the work of the internal quality commission, daily and summarized forms of reports on the work of the medical center, which have five sections are obtained. The statistical indicators of the operation of the surgical unit for any period are fixed, formed in an automated mode and analyzed. Clinical data characterizing all prescribed procedures applied are subject to analysis, and the results provide an opportunity to isolate from the records the disorders and complications reflected in the reports. The quality control of the use of antibiotics, the frequency of their use for preventive and therapeutic purposes is also carried out. The section that contains multivariate epidemiological control of the work of the treatment facility, as well as the section that shows the quality of medical record keeping and patient care is important. Together with the inpatient chart, e-mails of medical appointments are analyzed, patient falls are recorded, appointments and monitoring of patient care, pain assessment after surgery, allergic reactions are recorded, as well as a retrospective audit of the charts regarding the justification of the appointment of antimicrobial therapy. Control of patient safety and the quality of the provided medical services requires constant verification of their implementation and paying attention to be up to standards, the occurrence of deviations and complications. The application of a specially developed algorithm based on information technologies, aimed at daily accounting of medical services and obtaining data on deviations in their application, makes it possible to make urgent management decisions and influence the improvement of the qualifications of personnel who provide such services.

Raman Spectroscopic Analysis of Steviol Glycosides: Spectral Database and Quality Control Algorithms

Besides all sharing an extraordinary high (i.e., up to ~450 times) sweetening power as compared to sucrose and while presenting strong similarities in their molecular structures, molecules belonging to the family of diterpene glycosides (i.e., the secondary metabolites of Stevia rebaudiana) differ in specific structural details that strongly impact on their levels of sweetness and bitter aftertaste. Given the nutritional and pharmacological benefits of steviol secondary metabolites as natural dietetic and anti-diabetic remedies, extraction and purification of steviol glycosides from plant material are nowadays widely spread among many countries. However, an unpleasant bitter aftertaste, which is linked to a genetic variation in human bitter taste receptors, hampers the full exploitation of such benefits and calls for a prompt improvement in organoleptic property control of stevia products. A deeper understanding of the molecular structure of different steviol glycosides and the consequent development of promptly measurable criteria for the organoleptic performance of their mixtures will support processing optimization and control of taste profiles within desired yields. The present research aimed at establishing Raman spectroscopic algorithms for quantitative characterizations of raw stevia-based sweetener products. First, a series of twelve high-purity diterpene glycosides were analyzed by high spectrally resolved Raman spectroscopy and their spectra analyzed in order to establish a complete Raman library of molecular structures. Then, quantitative spectroscopic parameters were built up and applied to characterize the organoleptic property of five different commercially available samples including the recently developed Rebaudioside M isoform. Raman spectroscopy was confirmed as a versatile analytical technique that could be used for quantitative quality control tasks on the production line and for prompt in situ characterizations of purchased products.

Quantitative Testing of a SOLO-Based Automated Quality Control Algorithm for Airborne Tail Doppler Radar Data

An automated quality control pre-processing algorithm for removing non-weather radar echoes from airborne Doppler radar data has been developed. The proposed algorithm can significantly reduce the time and experience level required for interactive radar data editing prior to dual-Doppler wind synthesis or data assimilation. As important as reducing the time required and skill level necessary to process an airborne Doppler dataset can be, the quality of the automated analysis is paramount. Retrieved wind data, recovered perturbation pressure data (with associated momentum check values) and correlation coefficients were computed. To quantitatively test the quality of the automated quality control algorithm, spatial Pearson correlation coefficients and momentum check values were computed. Four different (published) Electra Doppler Radar (ELDORA) datasets of convective echoes were used to stress the algorithm. Four distinct threshold levels for data removal in the automated quality control algorithm were applied to each of four ELDORA datasets. The algorithm threshold levels were labeled as follows: extremely low, low, medium, and high. Extremely low algorithm cases were deemed necessary during the data analyses and were added to the low, medium and high cases. A description of each case and the differences in the perturbation pressure momentum check values and correlation coefficients between the interactively edited fields were computed. These comparisons along with a subjective visual inspection show that the automated quality control algorithm can produce an analysis comparable—and in some cases superior—to an interactive analysis when used properly. A key benefit of this algorithm is that the skill level of a relatively inexperienced airborne radar meteorologist may be effectively increased by using the SOLO QC algorithm.

Improving quality control of whole slide images by explicit artifact augmentation

The problem of artifacts in whole slide image acquisition, prevalent in both clinical workflows and research-oriented settings, necessitates human intervention and re-scanning. Overcoming this challenge requires developing quality control algorithms, that are hindered by the limited availability of relevant annotated data in histopathology. The manual annotation of ground-truth for artifact detection methods is expensive and time-consuming. This work addresses the issue by proposing a method dedicated to augmenting whole slide images with artifacts. The tool seamlessly generates and blends artifacts from an external library to a given histopathology dataset. The augmented datasets are then utilized to train artifact classification methods. The evaluation shows their usefulness in classification of the artifacts, where they show an improvement from 0.10 to 0.01 AUROC depending on the artifact type. The framework, model, weights, and ground-truth annotations are freely released to facilitate open science and reproducible research.

A new global daily sea‐surface temperature analysis system at Environment and Climate Change Canada

AbstractA new global daily sea‐surface temperature (SST) analysis system has been developed at Environment and Climate Change Canada (ECCC). All components of the new SST analysis system are implemented within the Modular and Integrated Data Assimilation System (MIDAS) software. MIDAS is already used for the data assimilation component of the main operational numerical weather prediction (NWP) systems at ECCC. The new SST analysis system, integrated together with the global sea‐ice analysis, will be part of the combined ocean surface analysis used for all operational prediction systems at ECCC. The data assimilation method used to compute the new SST analyses is two‐dimensional variational method with a diffusion operator for representing the horizontal background‐error correlations. A new algorithm for satellite data bias estimation has also been developed employing gridded bias estimates computed from a spatial averaging of the differences between collocated satellite and in‐situ data. New algorithms for quality control and thinning of satellite data have also been implemented, making each type of observational dataset more evenly distributed over the globe. The performance of the new SST system is examined relative to the current operational SST system by using independent data. The impact of using the new SST analysis within NWP and ocean prediction systems is also evaluated. When compared with the operational system currently in use, the experiments employing the new SST analysis system produce a nearly neutral impact on the NWP and ocean prediction systems. This validation of the new system is an important first step towards the ability to use MIDAS to perform ensemble‐based three‐dimensional ocean and coupled ocean‐ice–atmosphere data assimilation.

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens with known, unchangeable defect properties, which are usually complicated to fabricate. It consist of a shape memory polymer foil with temperature-dependent Young’s modulus and ultrasound attenuation. This open a possibility to generate a reconfigurable defect by projecting a heating laser in the form of a short line on the specimen surface. Ultrasound is generated by a laser pulse at one fixed position and detected by a laser vibrometer at another fixed position for 64 different defect positions and 3 different configurations of the specimen. The obtained diversified datasets are used to optimize the neural network architecture for the interpretation of ultrasound signals. We study the performance of the model in cases of reduced and dissimilar training datasets. In our first study, we classify the specimen configurations with the defect position being the disturbing parameter. The model shows high performance on a dataset of signals obtained at all the defect positions, even if trained on a completely different dataset containing signals obtained at only few defect positions. In our second study, we perform precise defect localization. The model becomes robust to the changes in the specimen configuration when a reduced dataset, containing signals obtained at two different specimen configurations, is used for the training process. This work highlights the potential of the demonstrated machine learning algorithm for industrial quality control. High-volume products (simulated by a reconfigurable specimen in our work) can be rapidly tested on the production line using this single-point and contact-free laser ultrasonic method.

Intelligent quality control of traditional chinese medical tongue diagnosis images based on deep learning.

Computer-aided tongue and face diagnosis technology can make Traditional Chinese Medicine (TCM) more standardized, objective and quantified. However, many tongue images collected by the instrument may not meet the standard in clinical applications, which affects the subsequent quantitative analysis. The common tongue diagnosis instrument cannot determine whether the patient has fully extended the tongue or collected the face. This paper proposes an image quality control algorithm based on deep learning to verify the eligibility of TCM tongue diagnosis images. We firstly gathered enough images and categorized them into five states. Secondly, we preprocessed the training images. Thirdly, we built a ResNet34 model and trained it by the transfer learning method. Finally, we input the test images into the trained model and automatically filter out unqualified images and point out the reasons. Experimental results show that the model's quality control accuracy rate of the test dataset is as high as 97.06%. Our methods have the strong discriminative power of the learned representation. Compared with previous studies, it can guarantee subsequent tongue image processing. Our methods can guarantee the subsequent quantitative analysis of tongue shape, tongue state, tongue spirit, and facial complexion.

A deep learning framework for quality control process in the motor oil industry

Given the advancements in the modern world, using Multivariate-Multistage Quality Control (MVMSQC) patterns in continuous production industries is deemed crucial and essential. This study examines the importance and necessity of Multivariate-Multistage Quality Control in manufacturing industries, focusing on motor oil production. Motor oil quality significantly influences engine performance. Thus, the primary objective of this research is to enhance accuracy in detecting faults in quality variables, utilizing deep learning algorithms for visual quality control of data, a facet often overlooked in classical statistical methods and reducing process control time. Combining deep learning algorithms such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) for controlling numerical variables, and Residual Networks (ResNet) and Dense Convolutional Networks (DenseNet) for controlling image variables have been employed. Additionally, the Honey Bee Mating Optimization Algorithm (GBC) has been utilized to tune the parameters of the LSTM-CNN and ResNet-DenseNet deep learning algorithms. Combining heuristic algorithms and deep learning algorithms enhances the performance of the final models in quality control processes. A case study in the motor oil production industry is examined to demonstrate the real-world application of the proposed model. The proposed LSTM-CNN hybrid algorithm outperforms individual CNN and LSTM algorithms in fault detection, improving performance by 15% and 8%, respectively. Furthermore, in visual components, the proposed ResNet-DenseNet hybrid algorithm has shown higher accuracy than ResNet and DenseNet algorithms, improving performance by 10% and 15%, respectively. This research significantly contributes to developing Multivariate-Multistage Quality Control in manufacturing industries by employing deep learning methods. The primary aim is to identify faults in quality components and utilize deep learning algorithms for visual quality control of data, a facet often overlooked in classical statistical methods. The research significantly advances multivariate-multistage quality control in manufacturing industries by leveraging deep learning methods. It addresses detecting complex nonlinear patterns in large-scale data with high flexibility and minimal time.

Enhanced patient-based real-time quality control using the graph-based anomaly detection.

Patient-based real-time quality control (PBRTQC) is an alternative tool for laboratories that has gained increasing attention. Despite the progress made by using various algorithms, the problems of data volume imbalance between in-control and out-of-control results, as well as the issue of variation remain challenges. We propose a novel integrated framework using anomaly detection and graph neural network, combining clinical variables and statistical algorithms, to improve the error detection performance of patient-based quality control. The testing results of three representative analytes (sodium, potassium, and calcium) and eight independent variables of patients (test date, time, gender, age, department, patient type, and reference interval limits) were collected. Graph-based anomaly detection network was modeled and used to generate control limits. Proportional and random errors were simulated for performance evaluation. Five mainstream PBRTQC statistical algorithms were chosen for comparison. The framework of a patient-based graph anomaly detection network for real-time quality control (PGADQC) was established and proven feasible for error detection. Compared with classic PBRTQC, the PGADQC showed a more balanced performance for both positive and negative biases. For different analytes, the average number of patient samples until error detection (ANPed) of PGADQC decreased variably, and reductions could reach up to approximately 95 % at a small bias of 0.02 taking calcium as an example. The PGADQC is an effective framework for patient-based quality control, integrating statistical and artificial intelligence algorithms. It improves error detection in a data-driven fashion and provides a new approach for PBRTQC from the data science perspective.

Promoting fairness and equality in higher education administration: A mathematical perspective on service quality control

This paper examines the integration of fairness and equality into higher education administration through a mathematical perspective on service quality control. By employing the Fairness Index, statistical equity measures, and quality control algorithms, the research evaluates the distribution of resources and opportunities among students and faculty. Additionally, the usage of the Gini coefficient and Lorenz curves aids in assessing service delivery equality. Through comprehensive data analysis from various higher education institutions, the study identifies notable disparities in administrative service quality, highlighting the need for policy reforms. Actionable recommendations are provided for administrators to enhance service quality while fostering a fair and equal academic environment.

Abstract 3765: Single cell spatial proteomics analysis and computational evaluation pipeline

Abstract Resolving tissue and proteomic heterogeneity is critical to decoding the structure and function of tumor-immune microenvironment (TIME). Such understanding requires profiling of tumor and immune cell proteomic features with spatial resolution at the single-cell level. Although such spatially resolved methods and data sets are becoming increasingly available, analytical and computational methods that can extract the highly complex features and interactions within TIME are lacking. To address that problem, we have developed a computational pipeline we call the Spatial Proteomics Analysis and Computational Evaluation Pipeline (SPACE). The SPACE pipeline is composed of many analysis modules for processing and mining highly multiplexed imaging-based data types to explore TIME composition, organization, and heterogeneity. The pipeline generates and interprets biomarker expression and positional information from multiplexed images using algorithms for image indexing, image registration, quality control, segmentation, identification and removal of non-specific signals, data normalization, automatic identification of missing data, and adjustment for left-over signals. The accurate intensity measurements at single cell level are then used to calculate the proposed spatial features that represent cellular interactions in TIME. A hierarchical decision tree of cell markers is used to annotate types and identities for individual cells. The SPACE enables statistical and differential analyses of complex spatial features as well as cell types/identities with respect to clinical annotations and genomic alterations. The visualization of spatial and imaging data is made possible through an open-source OMERO image repository and spatial maps that integrate diverse markers in a single representation. Here, we demonstrate the applications of our pipeline in diverse gastrointestinal tumor types (e.g., small bowel adenocarcinoma) and validate the importance of integrating tissue heterogeneity at spatial and single-cell level. The framework is applicable to nearly all highly multiplexed imaging data platforms, including but not limited to, CycIF, CODEX, and imaging mass cytometry. Citation Format: Behnaz Bozorgui, Zeynep Dereli, Guillaume Thibault, John N. Weinstein, Anil Korkut. Single cell spatial proteomics analysis and computational evaluation pipeline [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3765.

The Impact of Quality Control Methods on Vegetation Monitoring Using MODIS FPAR Time Series

Monitoring vegetation dynamics (VD) is crucial for environmental protection, climate change research, and understanding carbon and water cycles. Remote sensing is an effective method for large-scale and long-term VD monitoring, but it faces challenges due to changing data uncertainties caused by various factors, including observational conditions. Previous studies have demonstrated the significance of implementing proper quality control (QC) of remote sensing data for accurate vegetation monitoring. However, the impact of different QC methods on VD results (magnitude and trend) has not been thoroughly studied. The fraction of absorbed photosynthetically active radiation (FPAR) characterizes the energy absorption capacity of the vegetation canopy and is widely used in VD monitoring. In this study, we investigated the effect of QC methods on vegetation monitoring using a 20-year MODIS FPAR time series. The results showed several important findings. Firstly, we observed that the Mixed-QC (no QC on the algorithm path) generally produced a lower average FPAR during the growing season compared to Main-QC (only using the main algorithm). Additionally, the Mixed-QC FPAR showed a very consistent interannual trend with the Main-QC FPAR over the period 2002–2021 (p < 0.05). Finally, we found that using only the main algorithm for QC generally reduced the trend magnitude (p < 0.1), particularly in forests. These results reveal differences in FPAR values between the two QC methods. However, the interannual FPAR trends demonstrate greater consistency. In conclusion, this study offers a case study on evaluating the influence of different QC methods on VD monitoring. It suggests that while different QC methods may result in different magnitudes of vegetation dynamics, their impact on the time series trends is limited.

Marine Compass Data Quality Control Method Based on Robust Surface Fitting

Towed streamer positioning is a key step in offshore seismic exploration, and its positioning accuracy directly affects the imaging quality of subsequent seismic data. Accurate and clean magnetic compass observations are an important guarantee for high-precision towed streamer positioning. However, the existing compass data quality control methods have the problem of insufficient utilization of spatial distribution prior information, which heavily relies on mathematical statistical characteristics of the data while neglecting the crucial factors of streamer shape, bringing about an inadequate elimination of gross errors. In this work, a compass data quality control algorithm based on robust surface fitting is proposed to improve the accuracy of positioning. Firstly, the towed streamer positioning algorithm and the prior information of the spatial structure distribution of the compass are introduced. Secondly, the robust surface fitting to control the quality of compass data is proposed, and the specific process of the algorithm is given. Lastly, the effectiveness of the algorithm is verified by simulation and measured data. The results of simulation experiments indicate that compared with the traditional single-streamer threshold robust algorithm, the new algorithm has a significant effect on the quality control of compass data, and the positioning accuracy of the towed streamer is significantly improved. The positioning precision of along-line direction is improved by 52.1%, and that of the across-line direction by 52.4%. These demonstrate that the proposed new algorithm is effective for detecting and eliminating gross errors in compass observations in real-time operation scenarios, improving the stability of towed streamer shape calculation, which can better ensure the smooth progress of offshore towed streamer seismic exploration operations.

A quality-assured dataset of nine radiation components observed at the Shangdianzi regional GAW station in China (2013–2022)

Abstract. The New Baseline Surface Radiation (NBSR) system was established at the Shangdianzi (SDZ) regional Global Atmosphere Watch (GAW) station in 2013 to observe nine broadband radiation components, i.e. the global, direct, diffuse, and upwelling shortwave irradiance (GSWI, DSWI, DifSWI, and UpSWI); the photosynthetically active radiation (PAR); the ultraviolet irradiance (UVAI and UVBI); and the down- and upwelling longwave irradiance (DnLWI and UpLWI). To test the 1 min raw radiometric data, a Hybrid Algorithm for Radiation Data Quality Control (HARDQC) is presented in this study based on well-established methods, together with the solar irradiance dataset and the spectral features of the instrument bands. Subsequently, a NBSR dataset, which consists of radiation data at multiple timescales (i.e. 1 min, hourly, daily, monthly, monthly average hourly, and monthly average daily) over 2013–2022, is established and evaluated. Results show that more than 98.7 % of all radiation components passed the physical possibility test. The percentages of those that passed the extremely rare test are greater than 98.6 % for all radiation components except for the DnLWI (97.1 %). The percentages of those that passed the comparison test are greater than 83.3 % (GSWI), 78.3 % (DSWI), 81.7 % (DifSWI), 93.1 % (UpSWI), 88.9 % (PAR), 95.6 % (UVAI), 96.3 % (UVBI), 99.8 % (DnLWI), and 99.7 % (UpLWI), respectively. Due to data logger faults, removal of the instruments for calibration, and lightning strikes, some apparent data gaps in the upwelling radiation components (January 2015–August 2017) and all radiation components (December 2018; July to September 2021) were detected. Despite the existence of a few imperfections in the NBSR dataset, it is still reliable to apply it in many fields such as the validation of satellite products and numerical models, the investigation of relationships between radiation and atmospheric composition, and the detection of changes in the surface fluxes. The dataset described in this paper is available at https://doi.org/10.1594/PANGAEA.963330 (Quan et al., 2023b).

ARTIFICIAL INTELLIGENCE AS A LEARNING MEDIA IN ENGLISH EDUCATION: BIBLIOMETRIC USING BIBLIOSHINY ANALYSIS (2009-2023)

This research examines the publication trends and emerging keywords within the domain of artificial intelligence applied to English education. Analyzing 74 documents sourced from Scopus using RStudio and Vosviewer revealed distinct patterns. Notably, the year 2021 marked a significant milestone with 26 articles, constituting 35.14% of total publications, while the preceding decade (2009-2020) contributed only 13 articles, representing less than 18%. However, the period from 2021 to 2023 experienced a noteworthy surge, introducing over 80% of the total publications, incorporating 60 new articles. Esteemed institutions like Hebei University of Engineering in China and three other affiliates each contributed 4 publications. The prominence of the ACM International Conference Proceedings Series underscores the influential role of technology conferences in shaping discussions around artificial intelligence in English education. An influential article, cited 20 times, focused on the integration of 5G and AI technologies to reform English education at universities, considering the necessity of cutting-edge technological infrastructure. Visual analysis highlights specific keywords such as speech recognition, quality control, teaching systems, and learning algorithms within yellow circles, suggesting potential areas for further research in the intersection of artificial intelligence and English education.

Merging weather radar data and opportunistic rainfall sensor data to enhance rainfall estimates

Accurate rainfall estimates with high spatiotemporal resolution are vital for urban drainage applications, and if adjusted to ground observations, weather radar data has potential for these applications. The density of meteorological rain gauges for adjustment is, however, often sparse and non-uniformly distributed in space. Opportunistic rainfall sensors provide an increased density of ground observations but often with reduced or unknown accuracy for each individual station. This paper demonstrates the merging of data from weather radar, personal weather stations, and commercial microwave links into an integrated rainfall product. Merging opportunistic rainfall estimates is shown to improve the accuracy of the opportunistic rainfall observations through a quality control algorithm. In this study, we show that the accuracy of rainfall estimates is significantly improved by merging opportunistic rainfall data and weather radar data when compared to the accuracy of each rainfall product without merging. Nash-Sutcliffe efficiency (NSE) values up to 0.88 are obtained for daily accumulated merged rainfall products, while the NSE-values of the individual rainfall products range from −7.44 to 0.65, and similar tendencies are observed for root mean squared error (RMSE) values. For merging weather radar and opportunistic rainfall data, a novel approach, i.e., “moving median bias adjustment” is presented. Applying this approach, a high-performing rainfall product is derived independently from conventional high-quality rain gauges, which in this study are only used for independent validation. In addition, it is demonstrated that accurate rainfall estimates can be obtained by sub-daily merging, underscoring the importance of merging in nowcasting and near real-time applications.

Assessing the benefit of variational quality control for assimilating Aeolus Mie and Rayleigh wind profiles in NOAA's global forecast system during tropical cyclones

AbstractIn this article, we show a “proof‐of‐concept” study to assess the utility of a variational quality control algorithm in increasing the number of assimilated Aeolus Mie‐cloudy and Rayleigh‐clear winds in National Oceanic and Atmospheric Administration (NOAA)'s global data assimilation and forecast system. The National Centers for Environmental Prediction (NCEP) Variational Quality Control (NCEP‐VQC) algorithm was tuned and applied during the minimization process. This type of quality control uses optimal control theory principles to treat outliers in the probability density function (PDF) of observational departure statistics, assuming that the observation errors follow a family of logistic distributions. In the case of Aeolus Mie‐cloudy and Rayleigh‐clear winds, the NCEP‐VQC algorithm permitted the relaxation of the gross error and one of the recommended ESA quality controls (reject Rayleigh‐clear observations below 850 hPa), assigned adaptive observation weights ranging from 0 to 1, and led to an increase in the number of retained Aeolus observations for the calculation of global analyses, which in turn improved the verification statistics on analyzed tropical storms This article discusses the advantage of implementing the NCEP‐VQC algorithm in the Aeolus data assimilation, the benefits of retaining more wind profiles that contribute to the analysis calculation, and shows improvements in the initialization and short‐term forecasts on several tropical cyclone cases.

A Novel Quality Control Algorithm for Medical Image Segmentation Based on Fuzzy Uncertainty

Deep learning methods have achieved excellent performance in medical image segmentation. However, the practical application of deep learning based segmentation models is limited in clinical settings due to the lack of reliable information about the segmentation quality. In this paper, we propose a novel quality control algorithm based on fuzzy uncertainty to quantify the quality of the predicted segmentation results as part of the model inference process. Firstly, test-time augmentation and Monte Carlo dropout are applied simultaneously to capture both the data and model uncertainties of the trained image segmentation model. Then a fuzzy set is generated to describe the captured uncertainty with the assistance of the linear Euclidean distance transform algorithm. Finally, the fuzziness of the generated fuzzy set is adopted to calculate an image-level segmentation uncertainty and therefore to infer the segmentation quality. Extensive experiments using five medical image segmentation applications on the detection of skin lesion, nuclei, lung, breast and cell are conducted to evaluate the proposed algorithm. The experimental results show that the estimated image-level uncertainties using the proposed method have strong correlations with the segmentation qualities measured by the Dice coefficient, resulting in absolute Pearson correlation coefficients of 0.60-0.92. Our method outperforms other five state-of-the-art quality control methods in classifying the segmentation results into good and poor quality groups (area under the ROC of greater than 0.92, while other methods are below 0.85). The code is available on Github ( <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/qiaolin1992/Fuzzy-Uncertainty</uri> ).

A Comprehensive Corpus Callosum Segmentation Tool for Detecting Callosal Abnormalities and Genetic Associations from Multi Contrast MRIs.

Structural alterations of the midsagittal corpus callosum (midCC) have been associated with a wide range of brain disorders. The midCC is visible on most MRI contrasts and in many acquisitions with a limited field-of-view. Here, we present an automated tool for segmenting and assessing the shape of the midCC from T1w, T2w, and FLAIR images. We train a UNet on images from multiple public datasets to obtain midCC segmentations. A quality control algorithm is also built-in, trained on the midCC shape features. We calculate intraclass correlations (ICC) and average Dice scores in a test-retest dataset to assess segmentation reliability. We test our segmentation on poor quality and partial brain scans. We highlight the biological significance of our extracted features using data from over 40,000 individuals from the UK Biobank; we classify clinically defined shape abnormalities and perform genetic analyses.