Semi-automatic Method Research Articles

Cerebral AVM segmentation from 3D rotational angiography images by convolutional neural networks

Background and objective3D rotational angiography (3DRA) provides high quality images of the cerebral arteriovenous malformation (AVM) nidus that can be reconstructed in 3D. However, these reconstructions are limited to only 3D visualization without possible interactive exploration of geometric characteristics of cerebral structures. Refined understanding of the AVM angioarchitecture prior to treatment is mandatory and vascular segmentation is an important preliminary step that allow physicians analyze the complex vascular networks and can help guide microcatheters navigation and embolization of AVM. MethodsA deep learning method was developed for the segmentation of 3DRA images of AVM patients. The method uses a fully convolutional neural network with a U-Net-like architecture and a DenseNet backbone. A compound loss function, combining Cross Entropy and Focal Tversky, is employed for robust segmentation. Binary masks automatically generated from region-growing segmentation have been used to train and validate our model. ResultsThe developed network was able to achieve the segmentation of the vessels and the malformation and significantly outperformed the region-growing algorithm. Our experiments were performed on 9 AVM patients. The trained network achieved a Dice Similarity Coefficient (DSC) of 80.43%, surpassing other U-Net like architectures and the region-growing algorithm on the manually approved test set by physicians. ConclusionsThis work demonstrates the potential of a learning-based segmentation method for characterizing very complex and tiny vascular structures even when the training phase is performed with the results of an automatic or a semi-automatic method. The proposed method can contribute to the planning and guidance of endovascular procedures.

Quantification of coverage, uniformity and residues for CVD monolayer graphene transfer process based on image analysis

The use of CVD-grown graphene requires a transfer method to transport this material from the metal foil on top of which it is grown onto a target substrate. In many cases this transfer changes its properties, leading to variations from one zone of the target substrate to another. Here we present a simple semi-automatic method for a quantitative study of the coverage, uniformity and residues of graphene transferred onto Si/SiO2 substrates whose operation is not dependent on costly equipment, making it low-cost oriented. The method can be extrapolated to different target substrates, e.g., ITO-coated glass, and serves as an evaluation tool for graphene transfer processes. This is achieved by calculating a figure of merit, based on 2 proposed quantitative parameters (accounting for the coverage and the uniformity of the transferred graphene), while simultaneously determining the residues left by the transfer substrate using MATLAB-based image analysis of optical microscopy images. To corroborate the proposed method, a comparison between polydimethylsiloxane (PDMS), thermal release tape- (TRT) and polymethylmethacrylate- (PMMA) assisted transfer processes is performed, demonstrating that the methodology is valid for all cases. Coverages over 80 % are obtained, being in good agreement with results from the same transfer processes studied via Raman mapping.

Feasibility and reproducibility of semi-automated longitudinal strain analysis: a comparative study with conventional manual strain analysis

BackgroundConventional approach to myocardial strain analysis relies on a software designed for the left ventricle (LV) which is complex and time-consuming and is not specific for right ventricular (RV) and left atrial (LA) assessment. This study compared this conventional manual approach to strain evaluation with a novel semi-automatic analysis of myocardial strain, which is also chamber-specific.MethodsTwo experienced observers used the AutoStrain software and manual QLab analysis to measure the LV, RV and LA strains in 152 healthy volunteers. Fifty cases were randomly selected for timing evaluation.ResultsNo significant differences in LV global longitudinal strain (LVGLS) were observed between the two methods (-21.0% ± 2.5% vs. -20.8% ± 2.4%, p = 0.230). Conversely, RV longitudinal free wall strain (RVFWS) and LA longitudinal strain during the reservoir phase (LASr) measured by the semi-automatic software differed from the manual analysis (RVFWS: -26.4% ± 4.8% vs. -31.3% ± 5.8%, p < 0.001; LAS: 48.0% ± 10.0% vs. 37.6% ± 9.9%, p < 0.001). Bland–Altman analysis showed a mean error of 0.1%, 4.9%, and 10.5% for LVGLS, RVFWS, and LASr, respectively, with limits of agreement of -2.9,2.6%, -8.1,17.9%, and -12.3,33.3%, respectively. The semi-automatic method had a significantly shorter strain analysis time compared with the manual method.ConclusionsThe novel semi-automatic strain analysis has the potential to improve efficiency in measurement of longitudinal myocardial strain. It shows good agreement with manual analysis for LV strain measurement.Graphical

A Multi-Scale Rebar Detection Network with an Embedded Attention Mechanism

With the rapid development of computer vision and machine vision, methods based on deep learning have achieved good results in the field of object detection, identification, and tracking. However, for the detection and identification of rebars in smart construction sites, it is very difficult to perform accurate real-time detection of rebars by using object detection technology on the equipment in the field because of the problems of the dense cross-section between bundled bars, the mutual adhesion of cross-section boundaries, and mutual occlusion between cross-sections. To address the above problems, we propose a multi-scale rebar detection network RebarNet with an embedded attention mechanism based on YOLOv5, combining the K-means++ algorithm, attention mechanism, a newly designed SD_IoU Loss, and multi-scale feature fusion, aiming to solve the problems of missed and false detection in dense small object detection. Due to the problems of scarce rebar cross-section datasets, no publicly available large datasets, and weak rebar cross-sectional features, we constructed a new rebar cross-sectional dataset, used a semi-automatic annotation method to annotate part of the dataset, and then used the data enhancement algorithm to expand the rebar dataset. The experimental results show that the average accuracy (mAP) of our proposed RebarNet network is 97.9%, which is comparable to mainstream target detection algorithms such as Faster R-CNN, SSD, RetinaNet, CenterNet, CornerNet, YOLOv3, YOLOv4, and YOLOv5s. mAP0.5 is improved by 8.1%, 13%, 26.4%, 25.8%, 26.2%, 11.7%, 7.6%, and 9%, respectively. In addition, the frames per second (FPS) transmission reaches 89 frames per second, the model weight is only 17.0 MB. In summary, the proposed RebarNet can effectively reduce missed and false detections in the rebar counting detection task based on real-time detection.

Retinal Vessel Caliber Measurement Bias in Fundus Images in the Presence of the Central Light Reflex.

To investigate the agreement between a fundus camera and a scanning laser ophthalmoscope in retinal vessel caliber measurements and to identify whether the presence of the central light reflex (CLR) explains potential discrepancies. For this cross-sectional study, we obtained fundus camera and scanning laser ophthalmoscope images from 85 eyes of 85 healthy individuals (aged 50-65 years) with different blood pressure status. We measured the central retinal artery equivalent (CRAE) and central retinal artery vein equivalent (CRVE) with the Knudtson-Parr-Hubbard algorithm and assessed the CLR using a semiautomatic grading method. We used Bland-Altman plots, 95% limits of agreement, and the two-way mixed effects intraclass correlation coefficient for consistency [ICC(3,1)] to describe interdevice agreement. We used multivariable regression to identify factors associated with differences in between-device measurements. The between-device difference in CRAE (9.5 µm; 95% confidence interval, 8.0-11.1 µm) was larger than the between-device difference in CRVE (2.9 µm; 95% confidence interval, 1.3-4.5 µm), with the fundus camera yielding higher measurements (both P < 0.001). The 95% fundus camera-scanning laser ophthalmoscope limits of agreement were -4.8 to 23.9 µm for CRAE and -12.0 to 17.8 µm for CRVE. The corresponding ICCs(3,1) were 0.89 (95% confidence interval, 0.83-0.92) and 0.91 (95% confidence interval, 0.86-0.94). The between-device CRAE difference was positively associated with the presence of a CLR (P = 0.002). Fundus cameras and scanning laser ophthalmoscopes yield correlated but not interchangeable caliber measurements. The CLR induces bias in arteriolar caliber in fundus camera images, compared with scanning laser ophthalmoscope images. Refined measurements could yield better estimates of the association between retinal vessel caliber and ophthalmic or systemic disease.

First Ocean Wave Retrieval from HISEA-1 SAR Imagery through an Improved Semi-Automatic Empirical Model

The HISEA-1 synthetic aperture radar (SAR) minisatellite has been orbiting for over two years since its launch in 2020, acquiring numerous high-resolution images independent of weather and daylight. A typical and important application is the observation of ocean waves, essential ocean dynamical phenomena. Here, we proposed a new semi-automatic empirical method to retrieve ocean wave parameters from HISEA-1 images. We first applied some automated processing methods to remove non-wave information and artifacts, which largely improves the efficiency and robustness. Then, we developed an empirical model to retrieve significant wave height (SWH) by considering the dependence of SWH on azimuth cut-off, wind speed, and information extracted from the cross-spectrum. Comparisons with the Wavewatch III (WW3) data show that the performance of the proposed model significantly improved compared to the previous semi-empirical model; the root mean square error, correlation, and scattering index are 0.45 m (0.63 m), 0.87 (0.75), and 18% (26%), respectively. Our results are also consistent well with those from the altimeter measurements. Further case studies show that this new ocean wave model is reliable even under typhoon conditions. This work first provides accurate ocean-wave products from HISEA-1 SAR data and demonstrates its ability to perform high-resolution observation of coasts and oceans.

Expressive Prosody in Patients With Focal Anterior Temporal Neurodegeneration.

Progressive focal anterior temporal lobe (ATL) neurodegeneration has been historically called semantic dementia. More recently, semantic variant primary progressive aphasia (svPPA) and semantic behavioral variant frontotemporal dementia (sbvFTD) have been linked with predominant left and right ATL neurodegeneration, respectively. Nonetheless, clinical tools for an accurate diagnosis of sbvFTD are still lacking. Expressive prosody refers to the modulation of pitch, loudness, tempo, and quality of voice used to convey emotional and linguistic information and has been linked to bilateral but right-predominant frontotemporal functioning. Changes in expressive prosody can be detected with semiautomated methods and could represent a useful diagnostic marker of socioemotional functioning in sbvFTD. Participants underwent a comprehensive neuropsychological and language evaluation and a 3T MRI at the University of California San Francisco. Each participant provided a verbal description of the picnic scene from the Western Aphasia Battery. The fundamental frequency (f0) range, an acoustic measure of pitch variability, was extracted for each participant. We compared the f0 range between groups and investigated associations with an informant-rated measure of empathy, a facial emotion labeling task, and gray matter (GM) volumes using voxel-based morphometry. Twenty-eight patients with svPPA, 18 with sbvFTD, and 18 healthy controls (HCs) were included. f0 range was significantly different across groups: patients with sbvFTD showed reduced f0 range in comparison with both patients with svPPA (mean difference of -1.4 ± 2.4 semitones; 95% CI -2.4 to -0.4]; p < 0.005) and HCs (mean difference of -1.9 ± 3.0 semitones; 95% CI -3.0 to -0.7]; p < 0.001). A higher f0 range was correlated with a greater informant-rated empathy (r = 0.355; p ≤ 0.05), but not facial emotion labeling. Finally, the lower f0 range was correlated with lower GM volume in the right superior temporal gyrus, encompassing anterior and posterior portions (p < 0.05 FWE cluster corrected). Expressive prosody may be a useful clinical marker of sbvFTD. Reduced empathy is a core symptom in sbvFTD; the present results extend this to prosody, a core component of social interaction, at the intersection of speech and emotion. They also inform the long-standing debate on the lateralization of expressive prosody in the brain, highlighting the critical role of the right superior temporal lobe.

Lineaments as a tool for decision-making in the optimal location of boreholes in the base zone: Case of the department of Abtouyour (republic of Chad)

This study has made it possible to highlight that the extraction of structural lineaments helps in the optimal decision-making in the implantation of boreholes in the basement zone. For this purpose, we used remote sensing, as a complementary tool in the extraction of structural lineaments and validated our results with the drillings carried out using electrical tomography carried out in the area. This semi-automatic method allowed us to understand the importance that remote sensing can play in the establishment of successful drilling in the basement zone. For this study we used software such as Excel for drilling data ARMAP 10.8 for GIS, PCI Geomatica software for the extraction of lineaments. The orientation of the lineaments was obtained using the rose diagram of the ROCKWORKS software. In addition, for our study we used images from Landsat 9+, ASTER, DEM, and STRM of 30m resolution which allowed us to develop thematic maps. Thus we were able to validate our Works with the representation by the profiles of electrical resistivity especially the electrical tomography made in the study area and the drillings carried out according to the lineaments extracted. The combination of different parameters in the middle of the basement in a GIS facilitated the spatial analysis, the results was good for the decision making to drill water well in basement area. This approach allowed us to understand that more than 53% of positive drilling intercepts lineaments while only 13% of negative drilling intercepts lineaments.

Co-Expression of Immunohistochemical Markers MRP2, CXCR4, and PD-L1 in Gallbladder Tumors Is Associated with Prolonged Patient Survival.

Gallbladder cancer (GBC) is a rare pathology in Western countries. However, it constitutes a relevant health problem in Asia and Latin America, with a high mortality in middle-aged Chilean women. The limited therapeutic options for GBC require the identification of targetable proteins with prognostic value for improving clinical management support. We evaluated the expression of targetable proteins, including three epithelial tumor markers, four proteins associated with multidrug and apoptosis resistance, and eleven immunological markers in 241 primary gallbladder adenocarcinomas. We investigated correlations between tumor marker expression, the primary tumor staging, and GBC patients' survival using automated immunohistochemistry, a semi-automatic method for image analysis, univariate and multivariate statistical analyses, and machine learning algorithms. Our data show a significant association between the expression of MRP2 (p = 0.0028), CXCR4 (p = 0.0423), and PD-L1 (p = 0.0264), and a better prognosis for patients with late-stage primary tumors. The expression of the MRP2/CXCR4/PD-L1 cluster of markers discriminates among short-, medium-, and long-term patient survival, with an ROC of significant prognostic value (AUC = 0.85, p = 0.0012). Moreover, a high MRP2/CXCR4/PD-L1 co-expression is associated with increased survival time (30 vs. 6 months, p = 0.0025) in GBC patients, regardless of tumor stage. Hence, our results suggest that the MRP2/CXCR4/PD-L1 cluster could potentially be a prognostic marker for GBC.

Combination of expert guidelines-based and machine learning-based approaches leads to superior accuracy of automated prediction of clinical effect of copy number variations

Clinical interpretation of copy number variants (CNVs) is a complex process that requires skilled clinical professionals. General recommendations have been recently released to guide the CNV interpretation based on predefined criteria to uniform the decision process. Several semiautomatic computational methods have been proposed to recommend appropriate choices, relieving clinicians of tedious searching in vast genomic databases. We have developed and evaluated such a tool called MarCNV and tested it on CNV records collected from the ClinVar database. Alternatively, the emerging machine learning-based tools, such as the recently published ISV (Interpretation of Structural Variants), showed promising ways of even fully automated predictions using broader characterization of affected genomic elements. Such tools utilize features additional to ACMG criteria, thus providing supporting evidence and the potential to improve CNV classification. Since both approaches contribute to evaluation of CNVs clinical impact, we propose a combined solution in the form of a decision support tool based on automated ACMG guidelines (MarCNV) supplemented by a machine learning-based pathogenicity prediction (ISV) for the classification of CNVs. We provide evidence that such a combined approach is able to reduce the number of uncertain classifications and reveal potentially incorrect classifications using automated guidelines. CNV interpretation using MarCNV, ISV, and combined approach is available for non-commercial use at https://predict.genovisio.com/.

Deep learning-aided extraction of outer aortic surface from CT angiography scans of patients with Stanford type B aortic dissection

BackgroundGuidelines recommend that aortic dimension measurements in aortic dissection should include the aortic wall. This study aimed to evaluate two-dimensional (2D)- and three-dimensional (3D)-based deep learning approaches for extraction of outer aortic surface in computed tomography angiography (CTA) scans of Stanford type B aortic dissection (TBAD) patients and assess the speed of different whole aorta (WA) segmentation approaches.MethodsA total of 240 patients diagnosed with TBAD between January 2007 and December 2019 were retrospectively reviewed for this study; 206 CTA scans from 206 patients with acute, subacute, or chronic TBAD acquired with various scanners in multiple different hospital units were included. Ground truth (GT) WAs for 80 scans were segmented by a radiologist using an open-source software. The remaining 126 GT WAs were generated via semi-automatic segmentation process in which an ensemble of 3D convolutional neural networks (CNNs) aided the radiologist. Using 136 scans for training, 30 for validation, and 40 for testing, 2D and 3D CNNs were trained to automatically segment WA. Main evaluation metrics for outer surface extraction and segmentation accuracy were normalized surface Dice (NSD) and Dice coefficient score (DCS), respectively.Results2D CNN outperformed 3D CNN in NSD score (0.92 versus 0.90, p = 0.009), and both CNNs had equal DCS (0.96 versus 0.96, p = 0.110). Manual and semi-automatic segmentation times of one CTA scan were approximately 1 and 0.5 h, respectively.ConclusionsBoth CNNs segmented WA with high DCS, but based on NSD, better accuracy may be required before clinical application. CNN-based semi-automatic segmentation methods can expedite the generation of GTs.Relevance statementDeep learning can speeds up the creation of ground truth segmentations. CNNs can extract the outer aortic surface in patients with type B aortic dissection.Key points• 2D and 3D convolutional neural networks (CNNs) can extract the outer aortic surface accurately.• Equal Dice coefficient score (0.96) was reached with 2D and 3D CNNs.• Deep learning can expedite the creation of ground truth segmentations.Graphical

The impact of dry eye disease on corneal nerve parameters: A systematic review and meta-analysis.

Dry eye disease (DED) is a growing global health problem with a significant impact on the quality of life of patients. While neurosensory abnormalities have been recognised as a contributor to DED pathophysiology, the potential role of in vivo corneal confocal microscopy in detecting nerve loss or damage remains unclear. This systematic review with meta-analysis (PROSPERO registered CRD42022381861) investigated whether DED has an impact on sub-basal corneal nerve parameters. PubMed, Embase and Web of Science Core Collection databases were searched from inception to 9 December 2022. Studies using laser scanning confocal microscopy to compare corneal nerve parameters of DED with healthy eyes were included. Study selection process and data extraction were performed by two independent members of the review team. Twenty-two studies with 916 participants with DED and 491 healthy controls were included, with 21 of these studies included in subsequent meta-analyses. There was a decrease in total corneal nerve length (-3.85 mm/mm2 ; 95% CI -5.16, -2.55), corneal main nerve trunk density (-4.81 number/mm2 ; 95% CI -7.94, -1.68) and corneal nerve branch density (-15.52 number/mm2 ; 95% CI -27.20, -3.84) in DED eyes compared with healthy eyes, with subgroup analysis demonstrating that these differences were more evident in studies using NeuronJ software, a semi-automated procedure. While this review found evidence of loss of corneal nerve parameters in eyes with DED compared with healthy controls, particularly with the use of a semi-automated image analysis method, it is evident that there is substantial heterogeneity between studies in terms of corneal nerve imaging methodology. Standardisation is required in terms of terminology and analysis, with more research needed to potentially improve the clinical applicability and practicality of corneal nerve imaging. Further investigation is also required to confirm the diagnostic accuracy of this imaging modality and its potential for monitoring DED treatment efficacy.

A SEMI-AUTOMATED METHODOLOGY FOR 3D IMAGING OF LARGE VAULTED CEILING PAINTINGS

Abstract. 3D imaging of large painted and vaulted historic ceilings offers many challenges, including those due to complex small and large-scale geometry, environmental conditions and limited access. In this paper we introduce CHAPI (Cultural Heritage Automated Photogrammetric Imaging), a low cost solution to these problems which provides an efficient, semi-automated method of capturing large vaulted ceiling paintings in high detail, with a consistent photogrammetric network and in limited time. We will present and examine two different case studies from the Plafond3D project, the baroque ceiling paintings of the Schloss Rheinsberg Spiegelsaal (mirror room) and the Ansbach Residence Festsaal (great hall/ballroom). The paper will examine the particular challenges of capturing large painted areas with accurate colour and geometric reproduction, and suggest how the photogrammetric recording and reconstruction process of ceilings can be optimised for large rooms. This paper also gives technical specifications and Open Access Code for the CHAPI build.

VIRTUAL BIOMECHANICAL ASSESSMENT OF CUSTOM TRIFLANGE AND TRABECULAR METAL COMPONENTS TO TREAT LARGE ACETABULAR DEFECTS

The best treatment method of large acetabular bone defects at revision THR remains controversial. Some of the factors that need consideration are the amount of residual pelvic bone removed during revision; the contact area between the residual pelvic bone and the new implant; and the influence of the new acetabular construct on the centre of rotation of the hip. The purpose of this study was to compare these variables in two of the most used surgical techniques used to reconstruct severe acetabular defects: the trabecular metal acetabular revision system (TMARS) and a custom triflanged acetabular component (CTAC).Pre- and post-operative CT-scans were acquired from 11 patients who underwent revision THR with a TMARS construct for a Paprosky IIIB defect, 10 with pelvic discontinuity, at Royal Adelaide Hospital. The CT scans were used to generate computer models to virtually compare the TMARS and CTAC constructs using a semi-automated method. The TMARS construct model was calculated using postoperative CT scans while the CTAC constructs using the preoperative CT scans. The bone contact, centre of rotation, inclination, anteversion and reamed bone differences were calculated for both models.There was a significant difference in the mean amount of bone reamed for the TMARS reconstructions (15,997 mm3) compared to the CTAC reconstructions (2292 mm3, p&gt;0.01). There was no significant difference between overall implant bone contact (TMARS 5760mm2 vs CTAC 5447mm2, p=0.63). However, there was a significant difference for both cancellous (TMARS 4966mm2 vs CTAC 2887mm2, p=0.008) and cortical bone contact (TMARS 795mm2 vs CTAC 2560mm2, p=0.001). There was no difference in inclination and anteversion achieved. TMARS constructs resulted on average in a centre of rotations 7.4mm more lateral and 4.0mm more posterior.Modelling of two different reconstructions of Paprosky IIIB defects demonstrated potential important differences between all variables investigated.

18F-FDG and 18F-NaF PET/CT Global Assessment of Large Joint Inflammation and Bone Turnover in Rheumatoid Arthritis.

Rheumatoid arthritis (RA) involves chronic inflammation of synovial joints, causing pain, stiffness, and limited mobility. 18F-sodium fluoride (NaF) is a PET tracer whose uptake reflects bone turnover, while 18F-fludeoxyglucose (FDG) shows glucose metabolism and can serve as a marker for inflammation. The aim of this study is to determine the feasibility of calculating the FDG and NaF mean standardized uptake value (SUVmean) in the knee joint, hip joint, and sacroiliac (SI) joint of RA patients and to determine their association with patient characteristics. Prospective FDG-PET/CT as well as NaF-PET/CT imaging was performed on 18 RA patients. The global SUVmean was calculated on FDG-PET/CT and NaF-PET/CT images using a semiautomated CT-based method of segmentation. FDG and NaF uptake were found to be significantly correlated in the knee (r = 0.77, p < 0.001), but not in the hip and SI joints. In the knee, both NaF SUVmean and FDG SUVmean were significantly correlated with body weight, BMI, leptin, and sclerostin levels (p < 0.05). NaF SUVmean was significantly positively correlated with BMI and leptin for both the hip and SI joints (p < 0.05). No significant correlation was observed between either PET parameter and age, height, erythrocyte sedimentation rate (ESR), and interleukins 1 and 6 (IL-1 and IL-6); however, FDG was correlated with inflammatory markers such as C-reactive protein (CRP) and patient global visual analogue scale (VAS-PtGlobal) in some joints. In this study, both FDG and NaF uptake were quantified in large joints of patients with RA using CT segmentation. NaF and FDG SUVmean were correlated with clinical variables related to body weight and adiposity, suggesting that degenerative joint disease may play a larger role in influencing the uptake of these tracers in large joints than RA disease activity. FDG and its correlation with markers of inflammation such as CRP and VAS-PtGlobal suggests that this tracer may serve as a more specific marker for RA disease activity than NaF. Larger prospective and longitudinal data are necessary to gain a better understanding of the roles of FDG and NaF in evaluating RA joint activity in these joints.

Effect of surface-partial-volume correction and adaptive threshold on segmentation of uroliths in computed tomography.

Computed tomography (CT) is used to diagnose urolithiasis, a prevalent condition. In order to establish the strongest foundation for the quantifiability of urolithiasis, this study aims to develop semi-automated urolithiasis segmentation methods for CT images that differ in terms of surface-partial-volume correction and adaptive thresholding. It also examines the diagnostic accuracy of these methods in terms of volume and maximum stone diameter. One hundred and one uroliths were positioned in an anthropomorphic phantom and prospectively examined in CT. Four different segmentation methods were developed and used to segment the uroliths semi-automatically based on CT images. Volume and maximum diameter were calculated from the segmentations. Volume and maximum diameter of the uroliths were measured independently by three urologists by means of electronic calipers. The average value of the urologists´ measurements was used as a reference standard. Statistical analysis was performed with multivariate Bartlett's test. Volume and maximum diameter were in very good agreement with the reference measurements (r>0.99) and the diagnostic accuracy of all segmentation methods used was very high. Regarding the diagnostic accuracy no difference could be detected between the different segmentation methods tested (p>0.55). All four segmentation methods allow for accurate characterization of urolithiasis in CT with respect to volume and maximum diameter of uroliths. Thus, a simple thresholding approach with an absolute value may suffice for robust determination of volume and maximum diameter in urolithiasis.

End-to-Side vs. Free Graft Nerve Reconstruction-Experimental Study on Rats.

The long history of regeneration nerve research indicates many clinical problems with surgical reconstruction to be resolved. One of the promising surgical techniques in specific clinical conditions is end-to-side neurorrhaphy (ETS), described and then repeated with different efficiency in the 1990s of the twentieth century. There are no reliable data on the quality of recipient nerve regeneration, possible donor nerve damage, and epineural window technique necessary to be performed. This research attempts to evaluate the possible regeneration after end-to-side neurorrhaphy, its quality, potential donor nerve damage, and the influence of epineural windows on regeneration efficiency. Forty-five female Wistar rats were divided into three equal groups, and various surgical technics were applied: A-ETS without epineural window, B-ETS with epineural window, and C-free graft reconstruction. The right peroneal nerve was operated on, and the tibial nerve was selected as a donor. After 24 weeks, the regeneration was evaluated by (1) footprint analysis every two weeks with PFI (peroneal nerve function index), TFI (tibial nerve function index), and SFI (sciatic nerve function index) calculations; (2) the amplitude and latency measurements of motor evoked potentials parameters recorded on both sides of the peroneal and tibial nerves when electroneurography with direct sciatic nerve electrical stimulation and indirect magnetic stimulation were applied; (3) histomorphometry with digital conversion of a transverse semithin nerve section, with axon count, fibers diameter, and calculation of axon area with a semiautomated method were performed. There was no statistically significant difference between the groups investigated in all the parameters. The functional indexes stabilized after eight weeks (PFI) and six weeks (TFI and SFI) and were positively time related. The lower amplitude of tibial nerve potential in groups A and B was proven compared to the non-operated side. Neurophysiological parameters of the peroneal nerve did not differ significantly. Histomorphometry revealed significantly lower diameter and area of axons in operated peroneal nerves compared to non-operated nerves. The axon count was at a normal level in every group. Tibial nerve parameters did not differ from non-operated values. Regeneration of the peroneal nerve after ETS was ascertained to be at the same level as in the case of free graft reconstruction. Peroneal nerves after ETS and free graft reconstruction were ascertained to have a lower diameter and area than non-operated ones. The technique of an epineural window does not influence the regeneration result of the peroneal nerve. The tibial nerve motor evoked potentials were characterized by lower amplitudes in ETS groups, which could indicate axonal impairment.

A semiautomated risk assessment method for consumer products.

In this study, we develop a model that assesses product risk using online reviews from Amazon.com. We first identify unique words and phrases capable of identifying hazards. Second, we estimate risk severity using hazard type weights and risk likelihood using total reviews as a proxy for sales volume. In addition, we obtain expert assessments of product hazard risk (risk likelihood and severity) from a sample of high- and low-risk consumer products identified by a computerized risk assessment model we have developed. Third, we assess the validity of our computerized product risk assessment scoring model by utilizing the experts' survey responses. We find that our model is especially consistent with expert judgments of hazard likelihood but not as consistent with expert judgments of hazard severity. This model helps organizations to determine the risk severity, risk likelihood, and overall risk level of a specific product. The model produced by this study is helpful for product safety practitioners in product risk identification, characterization, and mitigation.

A State-of-the-art of Scientific Research on Disinformation

Technological advancements in information and communications technologies and related hardware and software have positively transformed the political, military, economic and social domains in all countries around the globe. These technologies are imperfect, and States and state-sponsored threat actors are exploiting flaws in hardware and software for various types of attacks. Furthermore, the same threat actors exploit software technologies to spread disinformation and disseminate false information to mislead public opinion. This research article reviews the discourse of the scientific community on disinformation. The purpose is to understand where the research focus lies and who the researchers are the co-authors, and the publication venues. This research article reviews the scientific literature using the computational literature review, a semi-automated review method and the structural topical modelling framework to understand trends in the research. Of 3 097 documents published in 1 700 publication venues between 1974 to 2022, 704 were analysed. The results reveal 46 topics on issues such as rumours and disinformation spread during the Covid-19 pandemic, Soviet and Russian Information Warfare, and Trolls and health-related themes and effects.

A Quality Control Improvement Model That Takes into Account the Sustainability Concept and KPIs

Nowadays, in addition to quality aspects, social and environmental issues are also a source of competitive advantage, as a result of changing socio-economic conditions and observed trends. The purpose of the study was to create a model for improving quality control in line with the concept of sustainable development. The model captures the microfoundational approach, and allows identifying quality capability with the idea of sustainable development. The proposed method is based on analyses of the characteristic parameters of methods for detecting aluminum casting nonconformities using automated or semi-automated nondestructive methods. The interdisciplinary approach takes into account indicators from the leading areas of sustainable development (economic, environmental and social dimensions) to determine the gradation of detection methods. Verification of the model confirmed its usefulness in foundry companies. As a result, a ranking of the total efficiency of the analyzed detection methods used in the quality control of the studied product was obtained. This enabled the development of energy, emission and cost optimization of the inspection process while maintaining the expected level of quality. Further research directions will concern the expansion of the model with additional key indicators related to the concept of pure quality control and automation of the implementation of analysis.