Automated Quality Control Research Articles

Automatic detection surface defects based on convolutional neural networks and deflectometry

AbstractSurface defects in industrial refrigerator manufacturing processes can cause significant production losses and compromise product quality. This area is underexplored and currently, visual quality inspection is a subjective process that requires expert intervention, which limits process efficiency and can lead to errors in defect detection. This paper presents a novel approach for automatic surface defect detection using a combination of convolutional neural networks (CNN) and deflectometry. The proposed method takes advantage of the high accuracy and robustness of CNNs in image classification tasks and the sensitivity of deflectometry to detect subtle surface variations. First, a prototype was built to get the images from the refrigerator. Second, using video recordings, we captured surface topographic data using deflectometry, which we then use to generate surface images. Next, we train a CNN to classify the surface images as defective or normal. The proposed method offers a promising solution for automatic detection and quality control of surface defects in refrigerator manufacturing processes. However, this method could also improve the production of vehicles, household appliances in general, and any product that can suffer scratches and dents.

Automatic quality control of aluminium parts welds based on 3D data and artificial intelligence

Automatic quality control of aluminium parts welds based on 3D data and artificial intelligence

Digital by design approach to develop a universal deep learning AI architecture for automatic chromatographic peak integration.

Chromatographic data processing has garnered attention due to multiple Food and Drug Administration 483 citations and warning letters, highlighting the need for a robust technological solution. The healthcare industry has the potential to greatly benefit from the adoption of digital technologies, but the process of implementing these technologies can be slow and complex. This article presents a "Digital by Design" managerial approach, adapted from pharmaceutical quality by design principles, for designing and implementing an artificial intelligence (AI)-based solution for chromatography peak integration process in the healthcare industry. We report the use of a convolutional neural network model to predict analytical variability for integrating chromatography peaks and propose a potential GxP framework for using AI in the healthcare industry that includes elements on data management, model management, and human-in-the-loop processes. The component on analytical variability prediction has a great potential to enable Industry 4.0 objectives on real-time release testing, automated quality control, and continuous manufacturing.

Automatic quality control of the numerical accuracy of EEG lead fields

Precise individualized EEG source localization is predicated on having accurate subject-specific Lead Fields (LFs) obtained from their Magnetic Resonance Images (MRI). LF calculation is a complex process involving several error-prone steps that start with obtaining a realistic head model from the MRI and finalizing with computationally expensive solvers such as the Boundary Element Method (BEM) or Finite Element Method (FEM). Current Big-Data applications require the calculation of batches of hundreds or thousands of LFs. LF. Quality Control is conventionally checked subjectively by experts, a procedure not feasible in practice for larger batches. To facilitate this step, we introduce the Lead Field Automatic-Quality Control Index (LF-AQI) that flags LF with potential errors. We base our LF-AQI on the assumption that LFs obtained from simpler head models, i.e., the homogeneous head model LF (HHM-LF) or spherical head model LF (SHM-LF), deviate only moderately from a "good" realistic test LF. Since these simpler LFs are easier to compute and check for errors, they may serve as "reference LF" to detect anomalous realistic test LF. We investigated this assumption by comparing correlation-based channel ρmin(ref,test)and source τmin(ref,test) similarity indices (SI) between "gold standards," i.e., very accurate FEM and BEM LFs, and the proposed references (HHM-LF and SHM-LF). Surprisingly we found that the most uncomplicated possible reference, HHM-LF had high SI values with the gold standards—leading us to explore further use of the channel ρmin(HHM−LF,test)and source τmin(HHM−LF,test)SI as a basis for our LF-AQI. Indeed, these SI successfully detected five simulated scenarios of LFs artifacts. This result encouraged us to evaluate the SI on a large dataset and thus define our LF-AQI. We thus computed the SI of 1251 LFs obtained from the Child Mind Institute (CMI) MRI dataset. When ρmin(HHM−LF,test)and source τmin(HHM−LF,test) were plotted for all test subjects on a 2D space, most were tightly clustered around the median of a high similarity centroid (HSC), except for a smaller proportion of outliers. We define the LF-AQI for a given LF as the log Euclidean distance between its SI and the HSC median. To automatically detect outliers, the threshold is at the 90th percentile of the CMI LF-AQIs (-0.9755). LF-AQI greater than this threshold flag individual LF to be checked. The robustness of this LF-AQI screening was checked by repeated out-of-sample validation. Strikingly, minor corrections in re-processing the flagged cases eliminated their status as outliers. Furthermore, the "doubtful" labels assigned by LF-AQI were validated by neuroscience students using a Likert scale questionnaire designed to manually check the LF's quality. Item Response Theory (IRT) analysis was applied to the questionnaire results to compute an optimized model and a latent variable θ for that model. A linear mixed model (LMM) between the θ and LF-AQI resulted in an effect with a Cohen's d value of 1.3 and a p-value <0.001, thus validating the correspondence of LF-AQI with the visual quality control. We provide an open-source pipeline to implement both LF calculation and its quality control to allow further evaluation of our index.

Siam-VAE: A hybrid deep learning based anomaly detection framework for automated quality control of head CT scans.

An automated quality control (QC) system is essential to ensure streamlined head computed tomography (CT) scan interpretations that do not affect subsequent image analysis. Such a system is advantageous compared to current human QC protocols, which are subjective and time-consuming. In this work, we aim to develop a deep learning-based framework to classify a scan to be of usable or unusable quality. Supervised deep learning models have been highly effective in classification tasks, but they are highly complex and require large, annotated data for effective training. Additional challenges with QC datasets include - 1) class-imbalance - usable cases far exceed the unusable ones and 2) weak-labels - scan level labels may not match slice level labels. The proposed framework utilizes these weak labels to augment a standard anomaly detection technique. Specifically, we proposed a hybrid model that consists of a variational autoencoder (VAE) and a Siamese Neural Network (SNN). While the VAE is trained to learn how usable scans appear and reconstruct an input scan, the SNN compares how similar this input scan is to its reconstruction and flags the ones that are less similar than a threshold. The proposed method is more suited to capture the differences in non-linear feature structure between the two classes of data than typical anomaly detection methods that depend on intensity-based metrics like root mean square error (RMSE). Comparison with state-of-the-art anomaly detection methods using multiple classification metrics establishes superiority of the proposed framework in flagging inferior quality scans for review by radiologists, thus reducing their workload and establishing a reliable and consistent dataflow.

Abstract 5442: SlideQC: An AI-based tool for automated quality control of whole-slide digital pathology images

Abstract Introduction: Artifacts are often introduced during tissue collection and processing, slide preparation, and/or when generating whole slide images (WSI). The presence of artifacts has a negative impact on the digital pathology workflow as artifacts may hinder diagnostic reporting and can lead to false positive and false negative results when using image analysis algorithms or computer-aided diagnosis systems. Manual quality control of WSI is a time-consuming procedure and therefore automated quality control tools, which report and exclude artifacts, are highly desirable to streamline digital pathology workflows. To automate the quality control step, we developed SlideQC, an AI-based quality control tool that automatically detects, reports, and outlines artifacts such as air bubbles, dust/debris, folds, out-of-focus,and pen marks, in both research and clinical workflows. Methods: SlideQC was trained with a DenseNet-based network using 1984 annotations for artifacts including air bubbles, dust/debris, folds, out-of-focus, and pen markers, across 254 Haematoxylin and Eosin (H&amp;E) stained WSI from more than 9 tissue types. A set of 2048 annotations from synthetically generated out-of-focus images was added to supplement the training data. The performance of the SlideQC was evaluated on an external test cohort of 49 WSI H&amp;E images sourced from the open-source database ‘HistoQCRepo’, across 375 annotations (tissue and artifact), and compared with the performance of HistoQC, an open-source quality control tool for digital pathology slides. Results: On the external test cohort, SlideQC showed high sensitivity, specificity, and F1-score with average values of 0.93, 0.99, and 0.93, across the five artifact types. In the same cohort, HistoQC attained an average sensitivity, specificity, and F1-score of 0.65, 0.79, and 0.54, respectively. Conclusions: SlideQC achieved high sensitivity, specificity, and F1-score on an external test cohort. SlideQC can add efficiency gains to a workflow by performing quality control on 100% of slides rather than the currently manually performed on only a subset of the slides in clinical pathology departments. SlideQC can allowthe triaging and alerting of slides containing a high level of artifact within a digital pathology workflow. The tool can also be used to exclude the artifact region from downstream analysis by subsequent image analysis algorithms. Citation Format: Daniela Rodrigues, Stefan Reinhard, Therese Waldburger, Daniel Martin, Suzana Couto, Inti Zlobec, Peter Caie, Erik Burlingame. SlideQC: An AI-based tool for automated quality control of whole-slide digital pathology images. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5442.

Automated quality control tool for high-content imaging data by building 2D prediction intervals on reference biosignatures.

Recent advances in automated microscopy and image analysis enables quantitative profiling of cellular phenotypes (Cell Painting). It paves the way for studying the broad effects of chemical perturbations on biological systems at large scale during lead optimization. Comparison of perturbation biosignatures with biosignatures of annotated compounds can inform on both on- and off-target effects. When building databases with phenotypic profiles of thousands of compounds, it is vital to control the quality of Cell Painting assays over time. A tool for this to our knowledge does not yet exist within the imaging community. In this paper, we introduce an automated tool to assess the quality of Cell Painting assays by quantifying the reproducibility of biosignatures of annotated reference compounds. The tool learns the biosignature of those treatments from a historical dataset, and subsequently, it builds a two-dimensional probabilistic quality control (QC) limit. The limit will then be used to detect aberrations in new Cell Painting experiments. The tool is illustrated using simulated data and further demonstrated on Cell Painting data of the A549 cell line. In general, the tool provides a sensitive, detailed and easy-to-interpret mechanism to validate the quality of Cell Painting assays.

Do the Math: Making Mathematics in Wikipedia Computable.

Wikipedia combines the power of AI solutions and human reviewers to safeguard article quality. Quality control objectives include detecting malicious edits, fixing typos, and spotting inconsistent formatting. However, no automated quality control mechanisms currently exist for mathematical formulae. Spell checkers are widely used to highlight textual errors, yet no equivalent tool exists to detect algebraically incorrect formulae. Our paper addresses this shortcoming by making mathematical formulae computable. We present a method that (1) gathers the semantic information surrounding the context of each mathematical formulae, (2) provides access to the information in a graph-structured dependency hierarchy, and (3) performs automatic plausibility checks on equations. We evaluate the performance of our approach on 6,337 mathematical expressions contained in 104 Wikipedia articles on the topic of orthogonal polynomials and special functions. Our system, [Formula: see text], verified 358 out of 1,516 equations as error-free. [Formula: see text] successfully translated 27% of the mathematical expressions and outperformed existing translation approaches by 16%. Additionally, [Formula: see text] achieved an F1 score of .495 for annotating mathematical expressions with relevant textual descriptions, which is a significant step towards advancing searchability, readability, and accessibility of mathematical formulae in Wikipedia. A prototype of [Formula: see text] and the semantically enhanced Wikipedia articles are available at: https://tpami.wmflabs.org.

A new automatic quality control system for ocean profile observations and impact on ocean warming estimate

A new automatic quality control system for ocean profile observations and impact on ocean warming estimate

Possibility of infrared (IR) thermography camera to investigate properties of injected plastic product and optimisation injection process parameter using a design of experiment

ABSTRACT The injection processing parameters have an important role in product quality. The purpose of this research is to estimate the usage of infrared (IR) thermal camera in automatic quality control of polymer parts in the production lines compared to simulation. The evaluation of injected part properties monitored from an IR camera is studied in terms of the defect and mechanical properties. The optimisation of the processing parameters to increase mechanical properties and decrease injection defects is examined. There are four processing factors with four levels namely, melt temperature, mould temperature, injection pressure, and flow rate designed by using Taguchi’s experimental design. The effect of average temperature of injection moulded parts monitored by IR camera on defect and mechanical properties is determined. The variance (ANOVA) method is used to evaluate the influence of each factors. The parameter factors of all experiments are definitely different because of P-values less than 0.05. The optimum processing parameters of PP were a melting temperature of 193°C, mould temperature of 30°C, injection pressure of 50 bar, and flow rate of 40 cm3/s. The suitable processing conditions for ABS are melting temperature, mould temperature, injection pressure, and flow rate of 216°C, 50°C, 90 bar, and 20 cm3/s respectively. IR thermograph accurately monitors short shot, sink mark, and large flash in moulding part; moreover, IR camera can estimate the mechanical properties of ABS by average temperature but cannot accurately measure the mechanical properties of high glossy PP. It is meant that IR thermal imaging technique can automatically control the product quality in the injection moulding process.

Design of Automatic Pond Water Quality Control in Koi Fish Farm

Koi fish is a type of freshwater ornamental fish that has high economic value. Koi fish have attractive body colors and ideal body shapes so they have good sales prospects. However, koi fish farming has several obstacles, one of which is the low recovery rate and the relatively slow growth of fish. Water quality is the main parameter in the success of a fishing effort. Therefore, this study designed a tool that can help koi fish farmers in unifying the quality of pond water so that koi fish can grow and develop properly. This monitoring system is designed by implementing an automatic control monitoring system to produce good water quality for koi fish farming. Data obtained from water quality monitoring are parameters of pH, ammonia, and water temperature. The results of this cultivator's research can monitor the quality of fish pond water and immediately get water quality control automatically.

Optical character recognition on engineering drawings to achieve automation in production quality control

Introduction: Digitization is a crucial step towards achieving automation in production quality control for mechanical products. Engineering drawings are essential carriers of information for production, but their complexity poses a challenge for computer vision. To enable automated quality control, seamless data transfer between analog drawings and CAD/CAM software is necessary.Methods: This paper focuses on autonomous text detection and recognition in engineering drawings. The methodology is divided into five stages. First, image processing techniques are used to classify and identify key elements in the drawing. The output is divided into three elements: information blocks and tables, feature control frames, and the rest of the image. For each element, an OCR pipeline is proposed. The last stage is output generation of the information in table format.Results: The proposed tool, called eDOCr, achieved a precision and recall of 90% in detection, an F1-score of 94% in recognition, and a character error rate of 8%. The tool enables seamless integration between engineering drawings and quality control.Discussion: Most OCR algorithms have limitations when applied to mechanical drawings due to their inherent complexity, including measurements, orientation, tolerances, and special symbols such as geometric dimensioning and tolerancing (GD&amp;amp;T). The eDOCr tool overcomes these limitations and provides a solution for automated quality control.Conclusion: The eDOCr tool provides an effective solution for automated text detection and recognition in engineering drawings. The tool's success demonstrates that automated quality control for mechanical products can be achieved through digitization. The tool is shared with the research community through Github.

Demonstrating quality control procedures for fMRI in DPABI.

Quality control (QC) is an important stage for functional magnetic resonance imaging (fMRI) studies. The methods for fMRI QC vary in different fMRI preprocessing pipelines. The inflating sample size and number of scanning sites for fMRI studies further add to the difficulty and working load of the QC procedure. Therefore, as a constituent part of the Demonstrating Quality Control Procedures in fMRI research topic in Frontiers, we preprocessed a well-organized open-available dataset using DPABI pipelines to illustrate the QC procedure in DPABI. Six categories of DPABI-derived reports were used to eliminate images without adequate quality. After the QC procedure, twelve participants (8.6%) were categorized as excluded and eight participants (5.8%) were categorized as uncertain. More automatic QC tools were needed in the big-data era while visually inspecting images was still indispensable now.

Applying machine learning classifiers to automate quality assessment of paediatric dynamic susceptibility contrast (DSC-) MRI data.

Investigate the performance of qualitative review (QR) for assessing dynamic susceptibility contrast (DSC-) MRI data quality in paediatric normal brain and develop an automated alternative to QR. 1027 signal-time courses were assessed by Reviewer 1 using QR. 243 were additionally assessed by Reviewer 2 and % disagreements and Cohen's κ (κ) were calculated. The signal drop-to-noise ratio (SDNR), root mean square error (RMSE), full width half maximum (FWHM) and percentage signal recovery (PSR) were calculated for the 1027 signal-time courses. Data quality thresholds for each measure were determined using QR results. The measures and QR results trained machine learning classifiers. Sensitivity, specificity, precision, classification error and area under the curve from a receiver operating characteristic curve were calculated for each threshold and classifier. Comparing reviewers gave 7% disagreements and κ = 0.83. Data quality thresholds of: 7.6 for SDNR; 0.019 for RMSE; 3 s and 19 s for FWHM; and 42.9 and 130.4% for PSR were produced. SDNR gave the best sensitivity, specificity, precision, classification error and area under the curve values of 0.86, 0.86, 0.93, 14.2% and 0.83. Random forest was the best machine learning classifier, giving sensitivity, specificity, precision, classification error and area under the curve of 0.94, 0.83, 0.93, 9.3% and 0.89. The reviewers showed good agreement. Machine learning classifiers trained on signal-time course measures and QR can assess quality. Combining multiple measures reduces misclassification. A new automated quality control method was developed, which trained machine learning classifiers using QR results.

Benchmarking of automatic quality control checks for ocean temperature profiles and recommendations for optimal sets

Millions of in situ ocean temperature profiles have been collected historically using various instrument types with varying sensor accuracy and then assembled into global databases. These are essential to our current understanding of the changing state of the oceans, sea level, Earth’s climate, marine ecosystems and fisheries, and for constraining model projections of future change that underpin mitigation and adaptation solutions. Profiles distributed shortly after collection are also widely used in operational applications such as real-time monitoring and forecasting of the ocean state and weather prediction. Before use in scientific or societal service applications, quality control (QC) procedures need to be applied to flag and ultimately remove erroneous data. Automatic QC (AQC) checks are vital to the timeliness of operational applications and for reducing the volume of dubious data which later require QC processing by a human for delayed mode applications. Despite the large suite of evolving AQC checks developed by institutions worldwide, the most effective set of AQC checks was not known. We have developed a framework to assess the performance of AQC checks, under the auspices of the International Quality Controlled Ocean Database (IQuOD) project. The IQuOD-AQC framework is an open-source collaborative software infrastructure built in Python (available from https://github.com/IQuOD). Sixty AQC checks have been implemented in this framework. Their performance was benchmarked against three reference datasets which contained a spectrum of instrument types and error modes flagged in their profiles. One of these (a subset of the Quality-controlled Ocean Temperature Archive (QuOTA) dataset that had been manually inspected for quality issues by its creators) was also used to identify optimal sets of AQC checks. Results suggest that the AQC checks are effective for most historical data, but less so in the case of data from Mechanical Bathythermographs (MBTs), and much less effective for Argo data. The optimal AQC sets will be applied to generate quality flags for the next release of the IQuOD dataset. This will further elevate the quality and historical value of millions of temperature profile data which have already been improved by IQuOD intelligent metadata and observational uncertainty information (https://doi.org/10.7289/v51r6nsf).

Development and clinical evaluation of an online automated quality control system for improving laboratory quality management

BackgroundWe developed an efficient online automated quality control (AUTO QC) system and tested its feasibility on automatic laboratory assembly lines. MethodsAUTO QC is based on developed quality control software (Smart QC) and designed adaptable consumables before. We applied the system to two assembly lines in our laboratory. Using third-party quality control samples, we evaluated the impact of the online AUTO QC system on out-of-control rate, biosecurity risk, turnaround time (TAT) and cost. ResultsAUTO QC significantly decreased the occurrence rate of the Westgard quality control rules 13S/22S/R4s and 12S, representing out-of-control and warning, respectively. The out-of-control rates were reduced by 58%, and the potential biosecurity risk of the samples decreased by 90%. The AUTO QC implementation also reduced the median TAT (by 7 min), the number of full-time employees and the cost of the quality control samples (by 45%). ConclusionsThe total laboratory AUTO QC system can improve the quality and stability of QC testing and reduce cost.

Automatic detection of Gibbs artefact in MR images with transfer learning approach.

Quality control of magnetic resonance imaging includes image validation, which covers also artefact detection. The daily manual review of magnetic resonance images for possible artefacts can be time-consuming, so automated methods for computer-assisted quality assessment of magnetic resonance imaging need to be developed. The aim of this study was to develop automatic detection of Gibbs artefacts in magnetic resonance imaging using a deep learning method called transfer learning, and to demonstrate the potential of this approach for the development of an automatic quality control tool for the detection of such artefacts in magnetic resonance imaging. The magnetic resonance image dataset of the scanned phantom for quality assurance was created using a turbo spin-echo pulse sequence in the transverse plane. Images were created to include Gibbs artefacts of varying intensities. The images were annotated by two independent reviewers. The annotated dataset was used to develop a method for Gibbs artefact detection using the transfer learning approach. The VGG-16, VGG-19, and ResNet-152 convolutional neural networks were used as pre-trained networks for transfer learning and compared using 5-fold cross-validation. All accuracies of the classification models were above 97%, while the AUC values were all above 0.99, confirming the high quality of the constructed models. We show that transfer learning can be successfully used to detect Gibbs artefacts on magnetic resonance images. The main advantages of transfer learning are that it can be applied on small training datasets, the procedures to build the models are not so complicated, and they do not require much computational power. This shows the potential of transfer learning for the more general task of detecting artefacts in magnetic resonance images of patients, which consequently can improve and speed up the process of quality assessment in medical imaging practice.

ДОСВІД ЗАСТОСУВАННЯ ГІС-ТЕХНОЛОГІЙ ПРИ СТВОРЕННІ (ОНОВЛЕННІ) ЦИФРОВИХ ТОПОГРАФІЧНИХ КАРТ МАСШТАБУ 1:25000

The purpose of this work is to research the actual methodology of creating a digital version of the nomenclature sheet on a scale of 1:25000 based on aerospace data using modern software for the formation of a single database of topographic data on a scale of 1:25000. The basis of the study is the analysis of the possibilities of applying theoretical and practical approaches to the updating of topographic maps, the selection of vectorization methods,the use of tools for quick editing and geoprocessing of data, ensuring the implementation of a system of checks of the nomenclature sheet and automatic control and assessment of the quality of vectorization by using geoinformation technologies of the ArcGIS software. The article discusses the importance of research aimed at creating and updating electronic topographic maps of Ukraine. The authors investigate the processes of creating (updating) digital topographic maps on a scale of 1:50000 in order to form a single database of topographic maps on a scale of 1:25000. The key stages of creating (updating) electronic topographic maps on a scale of 1:25000 for Ukraine are described. Using the ArcGIS program, a practical example of updating a topographic map ofscale 1:25000 was developed, based on the application of geoinformation technologies, the use of scanned paper maps, recent aerial photographs and satel-lite images. Methodological aspects of vectorization, detailing, completeness of attributive information, topological consistency and consistency of data, as well as features of automated quality control of updated electronic topographic maps are analyzed. The results of the study confirm that the use of geoinformation technologies and process automation can provide an up-to-date topographic data base that is important for various fields, improving project planning and implementation. The created base of topographic data on a scale of 1:25000 takes into account the functionality of step-by-step vectorization, selection criteria, rules of topological relationships, and automated quality control using modern geoinformation technologies.

Detekcija defekta u tekstilu korišćenjem neuronskog klasifikatora sa povratnim širenjem

The textile products are affected by the defects during the manufacturing processes. It is also waste of the resources used for the production and in turn it affects the business. The manual inspection in defect detections is not encouraged these days in manufacturing process. The computer vision with machine learning algorithms in automated quality control system plays an important role in detecting defects in manufacturing process as well as analyzing the quality of products. Classification of defects in knitted fabric is an active area of research around the globe. This paper presents a classification method to detect defects such as holes and thick places in knitted fabric by applying artificial neural network algorithm. The artificial neural network algorithms learn from the input data after successful training process, it predicts the nature of the unknown samples in very fast and accurate way. The proposed work has been carried out in two phases. In the first phase the images of the defective samples of two classes were collected by a high-resolution camera. The color images of the samples were converted into grey scale images. The features were extracted from each grey scale image and stored in a database. In the second phase a neural classifier was trained with back-propagation neural Network (BPNN) algorithm on the training dataset. After successful training of the neural network on train dataset, the performance of the trained neural network was evaluated on the test dataset. Different experiments were carried out by increasing the number of training data samples; it was found that the best evaluation performance was obtained as 83.3%.

Automatic localisation and per-region quantification of traumatic brain injury on head CT using atlas mapping

Rationale and objectivesTo develop a method for automatic localisation of brain lesions on head CT, suitable for both population-level analysis and lesion management in a clinical setting. Materials and methodsLesions were located by mapping a bespoke CT brain atlas to the patient’s head CT in which lesions had been previously segmented. The atlas mapping was achieved through robust intensity-based registration enabling the calculation of per-region lesion volumes. Quality control (QC) metrics were derived for automatic detection of failure cases. The CT brain template was built using 182 non-lesioned CT scans and an iterative template construction strategy. Individual brain regions in the CT template were defined via non-linear registration of an existing MRI-based brain atlas.Evaluation was performed on a multi-centre traumatic brain injury dataset (TBI) (n = 839 scans), including visual inspection by a trained expert. Two population-level analyses are presented as proof-of-concept: a spatial assessment of lesion prevalence, and an exploration of the distribution of lesion volume per brain region, stratified by clinical outcome. Results95.7% of the lesion localisation results were rated by a trained expert as suitable for approximate anatomical correspondence between lesions and brain regions, and 72.5% for more quantitatively accurate estimates of regional lesion load. The classification performance of the automatic QC showed an AUC of 0.84 when compared to binarised visual inspection scores. The localisation method has been integrated into the publicly available Brain Lesion Analysis and Segmentation Tool for CT (BLAST-CT). ConclusionAutomatic lesion localisation with reliable QC metrics is feasible and can be used for patient-level quantitative analysis of TBI, as well as for large-scale population analysis due to its computational efficiency (<2 min/scan on GPU).