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The magnitude of the dipole effect detected by the cutoff energy measurement was modulated for SrTiO3 (STO)/LaAlO3 (LAO)/SrRuO3 (SRO)/STO (001) subs. epitaxial stacks by ultrathin SrAlOx (SAO) insertion prior to LAO deposition. The difference in frictional forces on the LAO surfaces between the stacks with and without SAO insertion was clearly detected by lateral force microscopy (LFM), which indicated the change in dominating surface atomic layers. From the statistical analysis of LFM images, the SAO insertion was found to improve the lateral uniformity of the stacking sequence of charged monatomic layers in epitaxial LAO along the c-axis [(LaO)+ and (AlO2)−], taking account of the inevitable correlation between the surface-terminating atoms with the stacking sequence of the charged monatomic layers in epitaxial LAO. These results are consistently explainable with the proposed model that the interface dipole effect along the c-axis of the perovskite epitaxial stack is determined by the stacking sequence of charged monatomic layers of LAO. [ABSTRACT FROM AUTHOR]

Photoelectron angular distributions (PADs) produced from the photoionization of chiral molecules using elliptically polarized light exhibit a forward/backward asymmetry with respect to the optical propagation direction. By recording these distributions using the velocity-map imaging (VMI) technique, the resulting photoelectron elliptical dichroism (PEELD) has previously been demonstrated as a promising spectroscopic tool for studying chiral molecules in the gas phase. The use of elliptically polarized laser pulses, however, produces PADs (and consequently, PEELD distributions) that do not exhibit cylindrical symmetry about the propagation axis. This leads to significant limitations and challenges when employing conventional VMI acquisition and data processing strategies. Using novel photoelectron image analysis methods based around Hankel transform reconstruction tomography and machine learning, however, we have quantified—for the first time—significant symmetry-breaking contributions to PEELD signals that are of a comparable magnitude to the symmetric terms in the multiphoton ionization of (1R,4R)-(+)- and (1S,4S)-(−)-camphor. This contradicts any assumptions that symmetry-breaking can be ignored when reconstructing VMI data. Furthermore, these same symmetry-breaking terms are expected to appear in any experiment where circular and linear laser fields are used together. This ionization scheme is particularly relevant for investigating dynamics in chiral molecules, but it is not limited to them. Developing a full understanding of these terms and the role they play in the photoionization of chiral molecules is of clear importance if the potential of PEELD and related effects for future practical applications is to be fully realized. [ABSTRACT FROM AUTHOR]

BACKGROUND: Roots, tubers and bananas (RTB) play an essential role as staple foods, particularly in Africa. Consumer acceptance for RTB products relies strongly on the functional properties of, which may be affected by the size and shape of its granules. Classically, these are characterized either using manual measurements on microscopic photographs of starch colored with iodine, or using a laser light‐scattering granulometer (LLSG). While the former is tedious and only allows the analysis of a small number of granules, the latter only provides limited information on the shape of the starch granule. RESULTS: In this study, an open‐source solution was developed allowing the automated measurement of the characteristic parameters of the size and shape of yam starch granules by applying thresholding and object identification on microscopic photographs. A random forest (RF) model was used to predict the starch granule shape class. This analysis pipeline was successfully applied to a yam diversity panel of 47 genotypes, leading to the characterization of more than 205 000 starch granules. Comparison between the classical and automated method shows a very strong correlation (R2 = 0.99) and an absence of bias for granule size. The RF model predicted shape class with an accuracy of 83%. With heritability equal to 0.85, the median projected area of the granules varied from 381 to 1115 μm2 and their observed shapes were ellipsoidal, polyhedral, round and triangular. CONCLUSION: The results obtained in this study show that the proposed open‐source pipeline offers an accurate, robust and discriminating solution for medium‐throughput phenotyping of yam starch granule size distribution and shape classification. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Effect of the presence of NaCl, Na2SO4, MgCl2 and CaCl2 as smart water with different salinity was investigated in the formation and stability of the emulsion. Three methods were used to evaluate the emulsion stability. The first method, the amount of water separated from each sample, after 14 days of emulsion preparation, was recorded and examined. Bottle test was the second method to evaluate the stability during which the emulsion samples were monitored for up to 14 days. In the third method, a microscopic image was taken from each sample, and the mean droplet area for each sample was recorded. By adding Na2SO4 to a concentration of 8000 ppm in distilled water, the most stable emulsion was obtained, which was completely stable for up to 48 h, and after two weeks of preparation of the emulsion, almost only 10% of the aqueous phase was separated from the emulsion. And the microscopic image showed that in this case, the mean droplet area is 17 μm2, which, compared to other salts in different concentrations, had the lowest amount. On the other hand, salt NaCl with a concentration of 40,000 ppm formed the most unstable emulsion, which after 14 days, almost the entire aqueous phase was separated from the emulsion. By microscopic image analysis, the average droplets area was 58 μm2 for NaCl with 40000 ppm concentration, which is the highest value compared to other samples. While seawater was used to make the emulsion, ten times diluted seawater formed the most stable emulsion. [ABSTRACT FROM AUTHOR]

Multiplexed bead assays for solution‐phase biosensing often encounter cross‐over reactions during signal amplification steps, leading to unwanted false positive and high background signals. Current solutions involve complex custom‐designed and costly equipment, limiting their application in simple laboratory setup. In this study, we introduce a straightforward protocol to adapt a multiplexed single‐bead assay to standard fluorescence imaging plates, enabling the simultaneous analysis of thousands of reactions per plate. This approach focuses on the design and synthesis of bright fluorescent and magnetic microspheres (MagSiGlow) with multiple fluorescent wavelengths serving as unique detection markers. The imaging‐based, single‐bead assay, combined with a scripted algorithm, allows the detection, segmentation, and co‐localization on average of 7500 microspheres per field of view across five imaging channels in less than one second. We demonstrate the effectiveness of this method with remarkable sensitivity at low protein detection limits (100 pg/mL). This technique showed over 85 % reduction in signal cross‐over to the solution‐based method after the concurrent detection of tumor‐associated protein biomarkers. This approach holds the promise of substantially enhancing high throughput biosensing for multiple targets, seamlessly integrating with rapid image analysis algorithms. [ABSTRACT FROM AUTHOR]

This article, titled "Magnetic Resonance Imaging of the Postoperative Shoulder: Easing the Pain," discusses the challenges faced by radiologists in interpreting postoperative shoulder pain due to various surgical techniques, altered shoulder anatomy, and metallic artifacts. The authors conducted a retrospective study of 96 cases referred for imaging with persistent postoperative shoulder pain, rotator cuff weakness, and movement restriction. The study found that adhesive capsulitis was the most common cause of symptoms after rotator cuff repair, while labral repair had cases of retear, failed Latarjet, and post-remplissage dislodged anchor. The authors emphasize the importance of a radiologist's report in managing complications and providing value to patient care. [Extracted from the article]

Recently, the offline‐trained Siamese pipeline has drawn wide attention due to its outstanding tracking performance. However, the existing Siamese trackers utilise offline training to extract 'universal' features, which is insufficient to effectively distinguish between the target and fluctuating interference in embedding the information of the two branches, leading to inaccurate classification and localisation. In addition, the Siamese trackers employ a pre‐defined scale for cropping the search candidate region based on the previous frame's result, which might easily introduce redundant background noise (clutter, similar objects etc.), affecting the tracker's robustness. To solve these problems, the authors propose two novel sub‐network spatial employed to spatial feature embedding for robust object tracking. Specifically, the proposed spatial remapping (SRM) network enhances the feature discrepancy between target and distractor categories by online remapping, and improves the discriminant ability of the tracker on the embedding space. The MAML is used to optimise the SRM network to ensure its adaptability to complex tracking scenarios. Moreover, a temporal information proposal‐guided (TPG) network that utilises a GRU model to dynamically predict the search scale based on temporal motion states to reduce potential background interference is introduced. The proposed two network is integrated into two popular trackers, namely SiamFC++ and TransT, which achieve superior performance on six challenging benchmarks, including OTB100, VOT2019, UAV123, GOT10K, TrackingNet and LaSOT, TrackingNet and LaSOT denoting them as SiamSRMC and SiamSRMT, respectively. Moreover, the proposed trackers obtain competitive tracking performance compared with the state‐of‐the‐art trackers in the attribute of background clutter and similar object, validating the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Imaging is used to monitor disease activity in small bowel Crohn's disease (CD). Magnetic Resonance Enterography is often employed as a first modality in the United Kingdom for assessment and monitoring; however, waiting times, cost, patient burden and limited access are significant. It is as yet uncertain if small bowel intestinal ultrasound (IUS) may be a quicker, more acceptable, and cheaper alternative for monitoring patients with CD. A clinical service evaluation of imaging pathways was undertaken at a single NHS site in England, United Kingdom. Data were collected about patients who were referred and underwent an imaging analysis for their IBD. Only patients who underwent a therapy change were included in the analysis. Data were collected from care episodes between 01 January 2021–30 March 2022. A combined total of 193 patient care episodes were reviewed, 107 from the IUS pathway and 86 from the MRE pathway. Estimated costs per patient in the IUS pathway was £78.86, and £375.35 per patient in the MRE pathway. The MRE pathway had an average time from referral to treatment initiation of 91 days (SD= ±61) with patients in the IUS pathway waiting an average of 46 days (SD= ±17). Findings from this work indicate that IUS is a potential cost-saving option when compared to MRE when used in the management of CD. This is in addition to the cost difference of the radiological modalities. A large, multicentre, prospective study is needed to validate these initial findings. What is already known on this topic – Ultrasound is a quick and accurate imaging investigation for patients living with Crohn's disease. Its effect on the cost utility of an Inflammatory Bowel Disease service is unknown. What this study adds – This work provides initial data suggesting that an ultrasound-based service may provide significant cost savings when compared to a magnetic resonance imaging-based service. How this study might affect research, practice, or policy – This work is part of a larger programme of work to investigate the barriers to wider ultrasound implementation in UK IBD services. This work will contribute to the design of an implementation and training package for intestinal ultrasound in the UK. [ABSTRACT FROM AUTHOR]

Coating thickness is a critical quality attribute of many coated tablets. Functional coatings ensure correct drug release kinetics or protection from light, while non-functional coatings are generally applied for cosmetic reasons. Traditionally, coating thickness is assessed indirectly via offline methods, such as weight gain or diameter growth. In the past decade, several methods, including optical coherence tomography (OCT) and Raman spectroscopy, have emerged to perform in-line measurements of various subclasses of coating formulations. However, there are some obstacles. For example, when using OCT, a major challenge is scattering pigments, such as titanium dioxide and iron oxide, which make the interface between the coating and the tablet core difficult to detect. This work explores novel OCT image evaluation techniques using unsupervised machine learning to compute image metrics. Certain image metrics of highly scattering coatings are correlated with the tablet thickness, and hence indirectly with the coating thickness. The method was demonstrated using a titanium dioxide rich coating formulation. The results are expected to be applicable to other scattering coatings and will significantly broaden the applicability of OCT to at-line and in-line coating thickness measurements of a much larger class of coating formulations. [Display omitted] • Optical coherence tomography for indirect coating thickness measurement of highly scattering coatings. • Machine learning for image analysis to extract and quantify light reflection. • Non-destructive coating thickness measurement through light reflection properties. [ABSTRACT FROM AUTHOR]

Purpose: Evaluation of 90Y liver radioembolization post-treatment clinical data using a whole-body Biograph Vision Quadra PET/CT to investigate the potential of protocol optimization in terms of scan time and dosimetry. Methods: 17 patients with hepatocellular carcinoma with median (IQR) injected activity 2393 (1348–3298) MBq were included. Pre-treatment dosimetry plan was based on 99mTc-MAA SPECT/CT with Simplicit90Y™ and post-treatment validation with Quadra using Simplicit90Y™ and HERMIA independently. Regarding the image analysis, mean and peak SNR, the coefficient of variation (COV) and lesion-to-background ratio (LBR) were evaluated. For the post-treatment dosimetry validation, the mean tumor, whole liver and lung absorbed dose evaluation was performed using Simplicit90Y and HERMES. Images were reconstructed with 20-, 15-, 10-, 5- and 1- min sinograms with 2, 4, 6 and 8 iterations. Wilcoxon signed rank test was used to show statistical significance (p < 0.05). Results: There was no difference of statistical significance between 20- and 5- min reconstructed times for the peak SNR, COV and LBR. In addition, there was no difference of statistical significance between 20- and 1- min reconstructed times for all dosimetry metrics. Lung dosimetry showed consistently lower values than the expected. Tumor absorbed dose based on Simplicit90Y™ was similar to the expected while HERMES consistently underestimated significantly the measured tumor absorbed dose. Finally, there was no difference of statistical significance between expected and measured tumor, whole liver and lung dose for all reconstruction times. Conclusion: In this study we evaluated, in terms of image quality and dosimetry, whole-body PET clinical images of patients after having been treated with 90Y microspheres radioembolization for liver cancer. Compared to the 20-min standard scan, the simulated 5-min reconstructed images provided equal image peak SNR and noise behavior, while performing also similarly for post-treatment dosimetry of tumor, whole liver and lung absorbed doses. [ABSTRACT FROM AUTHOR]

Bioimage data are generated in diverse research fields throughout the life and biomedical sciences. Its potential for advancing scientific progress via modern, data‐driven discovery approaches reaches beyond disciplinary borders. To fully exploit this potential, it is necessary to make bioimaging data, in general, multidimensional microscopy images and image series, FAIR, that is, findable, accessible, interoperable and reusable. These FAIR principles for research data management are now widely accepted in the scientific community and have been adopted by funding agencies, policymakers and publishers. To remain competitive and at the forefront of research, implementing the FAIR principles into daily routines is an essential but challenging task for researchers and research infrastructures. Imaging core facilities, well‐established providers of access to imaging equipment and expertise, are in an excellent position to lead this transformation in bioimaging research data management. They are positioned at the intersection of research groups, IT infrastructure providers, the institution´s administration, and microscope vendors. In the frame of German BioImaging – Society for Microscopy and Image Analysis (GerBI‐GMB), cross‐institutional working groups and third‐party funded projects were initiated in recent years to advance the bioimaging community's capability and capacity for FAIR bioimage data management. Here, we provide an imaging‐core‐facility‐centric perspective outlining the experience and current strategies in Germany to facilitate the practical adoption of the FAIR principles closely aligned with the international bioimaging community. We highlight which tools and services are ready to be implemented and what the future directions for FAIR bioimage data have to offer. [ABSTRACT FROM AUTHOR]

Recent advances in microscopy imaging and image analysis motivate more and more institutes worldwide to establish dedicated core‐facilities for bioimage analysis. To maximise the benefits research groups at these institutes gain from their core‐facilities, they should be established to fit well into their respective environment. In this article, we introduce common collaborator requests and corresponding potential services core‐facilities can offer. We also discuss potential competing interests between the targeted missions and implementations of services to guide decision makers and core‐facility founders to circumvent common pitfalls. LAY DESCRIPTION: Bioimage analysis has become a field of research which is essential for driving current scientific projects and discoveries in the life sciences. It is a fast‐moving, diverse and highly multidisciplinary domain. Hence, single individuals that are proficient in analysing image data are scarce and often overwhelmed with providing support for their local communities while also developing their individual career paths. To support these talents, their knowledge and expertise, institutes and decision makers are increasingly creating core‐facilities for bioimage analysis. The success of a core‐facility to preserve, incubate and spread knowledge in bioimage analysis can be ensured by providing an appropriate set of services the core‐facility offers. In our manuscript, we summarise the requirements which a bioimage analysis core‐facilities are confronted with, and the services it can provide to satisfy these. We introduce categories of services in order to help core‐facility founders, members and decision makers to define their strategy and tailor their core‐facilities towards the specific needs of their local communities. Lastly, we summarise a set of competing interests which may arise in the process of shaping or managing a core‐facility and provide argumentative support towards their resolution. [ABSTRACT FROM AUTHOR]

Plantar pressure, a common gait and foot biomechanics measurement, is typically analyzed using proprietary commercial software packages. Regional plantar pressure analysis is often reported in terms of underlying bony geometry, and recent advances in image processing and accessibility have made computed tomography, radiographs, magnetic resonance imaging, or other imaging methods more popular for incorporating bone analyses in biomechanics. Can a computed tomography-based regional mask provide comparable regional analysis to commercial plantar pressure software and can the increased flexibility of an in-house method obtain additional insight from common measurements? A plantar pressure analysis method was developed based on bony geometry from computed tomography scans to calculate peak pressure, pressure time integral incorporating sub-peak values, force time integral, pressure gradient, and pressure gradient angle. Static and dynamic plantar pressure were acquired for 4 subjects (male, 65 ± 2.4 years). Plantar pressure variables were calculated using commercial and computed tomography-based systems. Dynamic peak pressure, pressure time integral, and force-time integral computed using the bone-based software was 5 % (9kPa), 7 % (0.3kPa-s) and 13 % (0.3 N-s) different than the commercial software on average. Region masks of the metatarsals and toes differed between commercial and computed tomography-based software due to subject-specific bone geometry and toe shape. Pressure time integral values incorporating sub-peak pressure were higher and demonstrated higher relative hindfoot values compared to those without. Removing step-on frames to static pressure analysis decreased forefoot pressures. Regional maps of peak pressure and maximum pressure gradient demonstrate different peak locations. Computed tomography-based regional masks are comparable to commercial masks. Inclusion of static step-on frames and sub-peak pressures may change regional plantar pressure patterns. Differences in location of maximum pressure gradient and peak pressure may be useful for assessing subject specific injury risk. • Plantar pressure masks can be automatically generated from computed tomography • Locations of peak pressure gradients and peak pressures may be different • Including sub-peak pressures in pressure-time integral may change regional trends [ABSTRACT FROM AUTHOR]

The photo-elicitation method can provide rich insights into home-making – the process whereby residents use, modify and personalize domestic space. However, previous studies have prioritized the words of participants, gathered in follow-up interviews, over photographs themselves. This paper presents an alternative methodological approach. Specifically, we approach photographs as primary sources, with a focus on their composition. First, we demonstrate how formal elements of photographs can be identified, and their meanings analysed. We show that domestic photographs speak to people's relationships with their homes, and identify empirical insights into scale, light and absences. Second, we combine data from follow-up interviews with formal analysis of images to confirm these insights, and generate additional findings – e.g., regarding the negotiation of domestic architecture and the meanings of possessions. We conclude that photo-elicitation studies of home-making benefit from both visual and narrative insights into participants' homes, and that both types of data merit serious analysis. [ABSTRACT FROM AUTHOR]

Introduction: The aim of this work is to estimate the sensitivity, specificity, and misclassification rate of an automated retinal image analysis system (ARIAS) in diagnosing active diabetic macular edema (DME) and to identify factors associated with true and false positives. Methods: We conducted a cross-sectional study of prospectively enrolled patients with diabetes mellitus (DM) referred to a tertiary medical retina center for screening or management of DME. All patients underwent two-field fundus photography (macula- and disc-centered) with a true-color confocal camera; images were processed by EyeArt V.2.1.0 (Woodland Hills, CA, USA). Active DME was defined as the presence of intraretinal or subretinal fluid on spectral-domain optical coherence tomography (SD-OCT). Sensitivity and specificity and their 95% confidence intervals (CIs) were calculated. Variables associated with true (i.e., DME labeled as present by ARIAS + fluid on SD-OCT) and false positives (i.e., DME labeled as present by ARIAS + no fluid on SD-OCT) of active DME were explored. Results: A total of 298 eyes were included; 92 eyes (31%) had active DME. ARIAS sensitivity and specificity were 82.61% (95% CI 72.37–89.60) and 84.47% (95% CI 78.34–89.10). The misclassification rate was 16%. Factors associated with true positives included younger age (p = 0.01), shorter DM duration (p = 0.006), presence of hard exudates (p = 0.005), and microaneurysms (p = 0.002). Factors associated with false positives included longer DM duration (p = 0.01), worse diabetic retinopathy severity (p = 0.008), history of inactivated DME (p < 0.001), and presence of hard exudates (p < 0.001), microaneurysms (p < 0.001), or epiretinal membrane (p = 0.06). Conclusions: The sensitivity of ARIAS was diminished in older patients and those without DME-related fundus lesions, while the specificity was reduced in cases with a history of inactivated DME. ARIAS performed well in screening for naïve DME but is not effective in surveillance inactivated DME. [ABSTRACT FROM AUTHOR]

We develop a novel framework for the analysis of medical imaging data, including magnetic resonance imaging, functional magnetic resonance imaging, computed tomography and more. Medical imaging data differ from general images in two main aspects: (i) the sample size is often considerably smaller and (ii) the interpretation of the model is usually more crucial than predicting the outcome. As a result, standard methods such as convolutional neural networks cannot be directly applied to medical imaging analysis. Therefore, we propose the deep Kronecker network , which can adapt to the low sample size constraint and offer the desired model interpretation. Our approach is versatile, as it works for both matrix- and tensor-represented image data and can be applied to discrete and continuous outcomes. The deep Kronecker network is built upon a Kronecker product structure, which implicitly enforces a piecewise smooth property on coefficients. Moreover, our approach resembles a fully convolutional network as the Kronecker structure can be expressed in a convolutional form. Interestingly, our approach also has strong connections to the tensor regression framework proposed by Zhou et al. (2013) , which imposes a canonical low-rank structure on tensor coefficients. We conduct both classification and regression analyses using real magnetic resonance imaging data from the Alzheimer's Disease Neuroimaging Initiative to demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

The in situ imaging of the cutting process exhibits outstanding advantages in reconstructing the precise and visual thermoplastic deformation fields. The physical and geometric characteristics of deformation fields provide a deeper understanding of the cutting processes. In this paper, a mechanism-image hybrid analysis method is proposed to acquire the characteristics of the serrated chip deformation in the orthogonal cutting of TA15 titanium alloy based on in situ imaging. The established hybrid analysis method combines the shear-plane theory with the streamline method and image segmentation method, which realizes the identification of pixel coordinates of the main shear plane (MSP) and the primary shear zone (PSZ) and then the extraction of the physical and geometric variables from the digital image correlation (DIC) full-field measurements. Consequently, the variations of equivalent strain rate, strain, temperature, and the geometric characterizations of MSP and PSZ during an individual serration formation of TA15 titanium alloy were quantitatively investigated. It was found that the physical and geometric variables reached stability in the final stage of serration evolution and were averaged as the DIC-based equivalent characterizations to analyze the impact of cutting depth and tool rake angle. Meanwhile, the DIC-based equivalent characterizations were compared with the results obtained by the classical analytical models to illustrate the advantages of the DIC-based analysis. The findings also support that the established hybrid analysis method holds the potential to characterize the serrated chip formation of other materials and improve the models of PSZ. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the relative positions of modern soft contact lenses (SCLs) relative to the limbus/cornea and the pupil. Methods: Sixty images of the anterior eyes of 101 subjects were acquired over 10 s while participants fixated the centre of the camera lens located 33 cm in front of the eye in a well‐lit (300 lux) clinic. Custom validated image analysis software was used to locate the boundaries of the contact lenses, pupils and corneas (limbus). Horizontal and vertical relative positions of the contact lens, pupil and limbus were calculated from the fitted boundaries. Results: The mean (standard deviation) pupil and corneal diameters for all subjects were 3.84 mm, (0.83) and 11.97 mm (0.48), respectively. The mean [95% confidence interval] pupil centre was located 0.28 mm [0.26, 0.30] nasally and 0.07 mm [0.05, 0.10] superiorly to the corneal centre. Consistent with clinical observations, the contact lenses centred accurately relative to the corneal centre both nasally 0.04 mm [0.01, 0.07] and inferiorly −0.01 mm [−0.06, 0.03]. However, regardless of the eye, the contact lens was significantly (p < 0.001) decentred relative to the pupil centre both temporally −0.23 mm [−0.26, −0.20] and inferiorly −0.08 mm [−0.12, −0.04]. Decentration magnitudes were significantly correlated between the right and left eyes. Conclusions: Spherical SCLs centred well on the cornea but temporally and inferiorly from the primary line of sight (pupil centre), due to the differences in the location of the pupil and corneal centres. Contrary to some previous reports, there was no evidence that lens optics or material affected lens centration significantly. [ABSTRACT FROM AUTHOR]

Automated medical image analysis solutions should closely mimic complete human actions to be useful in clinical practice. However, more often an automated image analysis solution represents only part of a human task, which restricts its practical utility. In the case of ultrasound-based fetal biometry, an automated solution should ideally recognize key fetal structures in freehand video guidance, select a standard plane from a video stream and perform biometry. A complete automated solution should automate all three subactions. In this article, we consider how to automate the complete human action of first-trimester biometry measurement from real-world freehand ultrasound. In the proposed hybrid convolutional neural network (CNN) architecture design, a classification regression-based guidance model detects and tracks fetal anatomical structures (using visual cues) in the ultrasound video. Several high-quality standard planes that contain the mid-sagittal view of the fetus are sampled at multiple time stamps (using a custom-designed confident-frame detector) based on the estimated probability values associated with predicted anatomical structures that define the biometry plane. Automated semantic segmentation is performed on the selected frames to extract fetal anatomical landmarks. A crown–rump length (CRL) estimate is calculated as the mean CRL from these multiple frames. Our fully automated method has a high correlation with clinical expert CRL measurement (Pearson's p = 0.92, R -squared [ R 2] = 0.84) and a low mean absolute error of 0.834 (weeks) for fetal age estimation on a test data set of 42 videos. A novel algorithm for standard plane detection employs a quality detection mechanism defined by clinical standards, ensuring precise biometric measurements. [ABSTRACT FROM AUTHOR]

Shale matrix permeability is vital to the evaluation of shale petrophysical property. In this study, an analytical model that considers pore size distribution, tortuosity, adsorption, and slippage effects is developed, to calculate the permeability of shale samples. Specifically, field emission scanning electron microscope (FE-SEM), the ImageJ software, and the MATLAB programming and numeric computing environment are employed to experimentally characterize the pore structure and to fit and optimize the pore size distribution. The discrete pore size distributions of organic matter and inorganic matter pores for Longmaxi and Qiongzhusi shale samples are determined based on FE-SEM images. According to the sum of squares error (SSE), coefficient of determination (R2), root-mean-square error (RMSE), and outcome of the fit, the Gaussian 2 correlation function is optimized to fit the continuous pore size distribution. Finally, the permeabilities of five shale rock samples are calculated and compared with the measured results from the Longmaxi and Qiongzhusi Formations. The results illustrate that the multi-term Gaussian fit is more suitable for calculating the permeability of samples with multimodal pore size distributions than the multi-segment Gaussian fit. The calculated results from the pore size distribution are more accuracy than those from the representative mean pore radius. The influence of pore size distribution on permeability and how seepage mechanisms in different pore sizes work together to represent permeability changes at the core scale will be investigated in the future. The study provides a more reliable calculation of the shale matrix permeability compared to previous methods. [ABSTRACT FROM AUTHOR]

To improve the accuracy of data processing and interpretation, effective suppression of the regional background field and extraction of the local anomaly field for further analyses represent urgent issues that need to be solved in the wide-field electromagnetic methods. The present study proposes an anomaly separation technique based on the residual method to develop the basic idea of regional field separation in gravity and magnetic exploration. The proposed technique can effectively suppress the regional background field and highlight local anomalies, such as faults and thin layers in inversion imaging, overcoming the shortcomings of traditional interpretation methods. Based on the interpolation-cutting method, a dynamically improved interpolation-cutting operator is proposed to obtain the regional background field by separating it from the local anomaly field. Furthermore, one- and two-dimensional complex geoelectric models are designed to perform calculations, inverse imaging, and anomaly separation. It has been demonstrated that the local anomaly information extracted by the anomaly separation technology describes accurate strata, structures, and faults, obviously superior to the traditional processing results. The proposed method is used to separate the anomalies from the wide-field electromagnetic inversion imaging in the Changning area of the southern Sichuan Province. The local information of the structures, thin layers, and faults obtained by the anomaly separation results is highly consistent with the seismic profile, indicating that the proposed anomaly separation technology is effective and reliable. Further, the proposed technology can effectively improve the inversion imaging accuracy and the interpretation reliability of traditional wide-field electromagnetic methods; thus, it has important application value. [ABSTRACT FROM AUTHOR]

Landslide event inventories are one of the most critical datasets to increase knowledge on landslide occurrences. However, they are rarely available in various regions, especially in countries of the Global South. This study aims to generate rainfall-induced landslide event inventories and define the rainfall thresholds responsible for landslide occurrence at the national scale of Malawi, Africa. We mainly followed a three-step methodology to generate landslide inventories. First, we went through media reports to identify documented landslide events. Second, we used Sentinel-2 images to identify possible areas affected by landslides using automated change detection algorithms based on vegetation indices. Third, we manually went through optical images provided by Planet Lab and Google Earth and mapped landslides via visual image interpretation. Overall, we mapped 27 rainfall-induced landslide inventories between 2003 and 2022, with a total of 4709 individual landslides. We then analysed the Malawian terrain and identified two different landscape clusters (i.e. Cluster 1 and Cluster 2) showing similar morphometric and climatic conditions. Ultimately, we calculated the rainfall threshold for each landscape cluster. The minimum rainfall amounts responsible for landsliding correspond to 66 mm/two-day and 51 mm/day in Clusters 1 and 2, respectively. In this context, our paper not only presents and shares the first national-scale, digital rainfall-induced landslide event inventory database of Malawi but also suitable rainfall thresholds to be potentially exploited for a national scale landslide early warning system. A similar framework could be applied to generate landslide inventories for other data scarce regions. [ABSTRACT FROM AUTHOR]

Introduction: Senescent cells contribute to age‐related tissue deterioration, including the skin, which plays important roles in overall health and social interactions. This study aimed to assess the effects of the senotherapeutic peptide, OS‐01 (a.k.a. Pep 14), on skin. Methods: A 12‐week split‐face, double‐blinded, vehicle‐controlled study involving 22 participants was conducted. The OS‐01‐containing formulation was applied to one side of the face, while the other side received an identical control formulation lacking the peptide. Skin characteristics were assessed using instrumental measurements, expert clinical grading, and subjective questionnaires. Results: Results showed that the OS‐01 formulation significantly improved one aspect of skin barrier function, as evidenced by reduced trans‐epidermal water loss compared to both baseline and vehicle control. Expert grading and Antera 3D image analysis revealed a reduction in wrinkle appearance and indentation in the periorbital area, and improved skin texture and radiance on both sides of the face, with the OS‐01‐containing formulation demonstrating superior results. Participants also perceived improvements in skin hydration, smoothness, radiance, and overall appearance. Conclusion: The findings suggest that the OS‐01 formulation promotes skin health by strengthening the skin barrier, protecting against dehydration, reducing the appearance of wrinkles, and improving skin texture and radiance. These effects are likely attributed to the senotherapeutic properties of OS‐01 in reducing cellular senescence and its associated detrimental effects. [ABSTRACT FROM AUTHOR]

Background: Personalised multi-compartment dosimetry based on [99mTc]Tc-MAA is a valuable tool for planning 90Y radioembolization treatments. The establishment and effective application of dose–effect relationships in yttrium-90 (90Y) radioembolization requires [99mTc]Tc-MAA SPECT quantification ideally independent of clinical site. The purpose of this multi-centre phantom study was to evaluate inter-site variability of [99mTc]Tc-MAA imaging and evaluate a standardised imaging protocol. Data was obtained from the TARGET study, an international, retrospective multi-centre study including 14 sites across 8 countries. The impact of imaging related factors was estimated using a NEMA IQ phantom (representing the liver), and a uniformly filled cylindrical phantom (representing the lungs). Imaging was performed using site-specific protocols and a standardized protocol. In addition, the impact of implementing key image corrections (scatter and attenuation correction) in the site-specific protocols was investigated. Inter-site dosimetry accuracy was evaluated by comparing computed Lung Shunt Fraction (LSF) measured using planar imaging of the cylindrical and NEMA phantom, and contrast recovery coefficient (CRC) measured using SPECT imaging of the NEMA IQ phantom. Results: Regarding the LSF, inter-site variation with planar site-specific protocols was minimal, as determined by comparing computed LSF between sites (interquartile range 9.6–10.1%). A standardised protocol did not improve variation (interquartile range 8.4–9.0%) but did improve mean accuracy compared to the site-specific protocols (5.0% error for standardised protocol vs 8.8% error for site-specific protocols). Regarding the CRC, inter-system variation was notable for site-specific SPECT protocols and could not be improved by the standardised protocol (CRC interquartile range for 37 mm sphere 0.5–0.7 and 0.6–0.8 respectively), however the standardised protocol did improve accuracy of sphere:background determination. Implementation of key image corrections did improve inter-site variation (CRC interquartile range for 37 mm sphere 0.6–0.7). Conclusion: Eliminating sources of variability in image corrections between imaging protocols reduces inter-site variation in quantification. A standardised protocol was not able to improve consistency of LSF or CRC but was able to improve accuracy. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has triggered a global public health crisis, affecting hundreds of countries. With the increasing number of infected cases, developing automated COVID-19 identification tools based on CT images can effectively assist clinical diagnosis and reduce the tedious workload of image interpretation. To expand the dataset for machine learning methods, it is necessary to aggregate cases from different medical systems to learn robust and generalizable models. This paper proposes a novel deep learning joint framework that can effectively handle heterogeneous datasets with distribution discrepancies for accurate COVID-19 identification. We address the cross-site domain shift by redesigning the COVID-Net's network architecture and learning strategy, and independent feature normalization in latent space to improve prediction accuracy and learning efficiency. Additionally, we propose using a contrastive training objective to enhance the domain invariance of semantic embeddings and boost classification performance on each dataset. We develop and evaluate our method with two large-scale public COVID-19 diagnosis datasets containing CT images. Extensive experiments show that our method consistently improves the performance both datasets, outperforming the original COVID-Net trained on each dataset by 13.27% and 15.15% in AUC respectively, also exceeding existing state-of-the-art multi-site learning methods. [ABSTRACT FROM AUTHOR]

We present Svetlana (SuperVised sEgmenTation cLAssifier for NapAri), an open-source Napari plugin dedicated to the manual or automatic classification of segmentation results. A few recent software tools have made it possible to automatically segment complex 2D and 3D objects such as cells in biology with unrivaled performance. However, the subsequent analysis of the results is oftentimes inaccessible to non-specialists. The Svetlana plugin aims at going one step further, by allowing end-users to label the segmented objects and to pick, train and run arbitrary neural network classifiers. The resulting network can then be used for the quantitative analysis of biophysical phenoma. We showcase its performance through challenging problems in 2D and 3D and provide a comprehensive discussion on its strengths and limits. [ABSTRACT FROM AUTHOR]

We evaluate the performance of the Legacy Survey of Space and Time Science Pipelines Difference Image Analysis (DIA) on simulated images. By adding synthetic sources to galaxies on images, we trace the recovery of injected synthetic sources to evaluate the pipeline on images from the Dark Energy Science Collaboration Data Challenge 2. The pipeline performs well, with efficiency and flux accuracy consistent with the signal-to-noise ratio of the input images. We explore different spatial degrees of freedom for the Alard–Lupton polynomial-Gaussian image subtraction kernel and analyze for trade-offs in efficiency versus artifact rate. Increasing the kernel spatial degrees of freedom reduces the artifact rate without loss of efficiency. The flux measurements with different kernel spatial degrees of freedom are consistent. We also here provide a set of DIA flags that substantially filter out artifacts from the DIA source table. We explore the morphology and possible origins of the observed remaining subtraction artifacts and suggest that given the complexity of these artifact origins, a convolution kernel with a set of flexible bases with spatial variation may be needed to yield further improvements. [ABSTRACT FROM AUTHOR]

In response to the growing number of diabetes cases worldwide, Our study addresses the escalating issue of diabetic eye disease (DED), a significant contributor to vision loss globally, through a pioneering approach. We propose a novel integration of a Genetic Grey Wolf Optimization (G-GWO) algorithm with a Fully Convolutional Encoder-Decoder Network (FCEDN), further enhanced by a Kernel Extreme Learning Machine (KELM) for refined image segmentation and disease classification. This innovative combination leverages the genetic algorithm and grey wolf optimization to boost the FCEDN's efficiency, enabling precise detection of DED stages and differentiation among disease types. Tested across diverse datasets, including IDRiD, DR-HAGIS, and ODIR, our model showcased superior performance, achieving classification accuracies between 98.5% to 98.8%, surpassing existing methods. This advancement sets a new standard in DED detection and offers significant potential for automating fundus image analysis, reducing reliance on manual examination, and improving patient care efficiency. Our findings are crucial to enhancing diagnostic accuracy and patient outcomes in DED management. [ABSTRACT FROM AUTHOR]

Understanding wildfire spread in Canada is critical to promoting forest health and protecting human life and infrastructure. Quantifying fire spread from noisy images, where change-point boundaries separate regions of fire, is critical to accurately estimating fire spread rates. The challenge lies in denoising the fire images and accurately identifying highly non-linear fire lines without smoothing over boundaries. In this paper, we develop an iterative smoothing algorithm for change-point data that utilizes oversmoothed estimates of the underlying data generating process to inform re-smoothing. We demonstrate its effectiveness on simulated one- and two-dimensional change-point data, and robustness to response outliers. Then, we apply the methodology to fire spread images from laboratory micro-fire experiments and show that the regions fuel, burning and burnt-out are smoothed while boundaries are preserved. [ABSTRACT FROM AUTHOR]

Based on the rapid advances in additive manufacturing, micro‐patterned heterostructures of soft materials have become available that need to be characterized down to the nanoscale. Advanced function‐structure relationships are designed by direct 3D structuring of the object and – in the future – fine control over material functionality in 3D will produce complex functional objects. To control their design, fabrication and final structure, morphological and spectroscopical imaging in 3D at nanometer resolution are critically required. With examples of carbon‐based objects, it is demonstrated how serial ultramicrotomy, that is, cutting a large number of successive ultrathin sections, can be utilized to gain access to the interior of 3D objects. Array tomography, hierarchical imaging and correlative light and electron microscopy can bridge length scales over several orders of magnitude and provide multimodal information of the sample's inner structure. Morphology data derived from scanning electron microscopy are correlated with spectroscopy in analytical transmission electron microscopy and probe microscopy at nanometer resolution, using TEM‐electron energy loss spectroscopy and infrared‐scanning‐near‐field microscopy. The correlation of different imaging modalities and spectroscopy of carbon‐based materials in 3D provides a powerful toolbox of complementary techniques for understanding emerging functions from nanoscopic structuring. [ABSTRACT FROM AUTHOR]

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal human malignancies. Tissue microarrays (TMA) are an established method of high throughput biomarker interrogation in tissues but may not capture histological features of cancer with potential biological relevance. Topographic TMAs (T-TMAs) representing pathophysiological hallmarks of cancer were constructed from representative, retrospective PDAC diagnostic material, including 72 individual core tissue samples. The T-TMA was interrogated with tissue hybridization-based experiments to confirm the accuracy of the topographic sampling, expression of pro-tumourigenic and immune mediators of cancer, totalling more than 750 individual biomarker analyses. A custom designed Next Generation Sequencing (NGS) panel and a spatial distribution-specific transcriptomic evaluation were also employed. The morphological choice of the pathophysiological hallmarks of cancer was confirmed by protein-specific expression. Quantitative analysis identified topography-specific patterns of expression in the IDO/TGF-β axis; with a heterogeneous relationship of inflammation and desmoplasia across hallmark areas and a general but variable protein and gene expression of c-MET. NGS results highlighted underlying genetic heterogeneity within samples, which may have a confounding influence on the expression of a particular biomarker. T-TMAs, integrated with quantitative biomarker digital scoring, are useful tools to identify hallmark specific expression of biomarkers in pancreatic cancer. [ABSTRACT FROM AUTHOR]

The microvasculature facilitates gas exchange, provides nutrients to cells, and regulates blood flow in response to stimuli. Vascular abnormalities are an indicator of pathology for various conditions, such as compromised vessel integrity in small vessel disease and angiogenesis in tumors. Traditional immunohistochemistry enables the visualization of tissue cross-sections containing exogenously labeled vasculature. Although this approach can be utilized to quantify vascular changes within small fields of view, it is not a practical way to study the vasculature on the scale of whole organs. Three-dimensional (3D) imaging presents a more appropriate method to visualize the vascular architecture in tissue. Here we describe the complete protocol that we use to characterize the vasculature of different organs in mice encompassing the methods to fluorescently label vessels, optically clear tissue, collect 3D vascular images, and quantify these vascular images with a semi-automated approach. To validate the automated segmentation of vascular images, one user manually segmented one hundred random regions of interest across different vascular images. The automated segmentation results had an average sensitivity of 83±11% and an average specificity of 91±6% when compared to manual segmentation. Applying this procedure of image analysis presents a method to reliably quantify and characterize vascular networks in a timely fashion. This procedure is also applicable to other methods of tissue clearing and vascular labels that generate 3D images of microvasculature. [ABSTRACT FROM AUTHOR]

Segmenting autophagic bodies in yeast TEM images is a key technique for measuring changes in autophagosome size and number in order to better understand macroautophagy/autophagy. Manual segmentation of these images can be very time consuming, particularly because hundreds of images are needed for accurate measurements. Here we describe a validated Cellpose 2.0 model that can segment these images with accuracy comparable to that of human experts. This model can be used for fully automated segmentation, eliminating the need for manual body outlining, or for model-assisted segmentation, which allows human oversight but is still five times as fast as the current manual method. The model is specific to segmentation of autophagic bodies in yeast TEM images, but researchers working in other systems can use a similar process to generate their own Cellpose 2.0 models to attempt automated segmentations. Our model and instructions for its use are presented here for the autophagy community.Abbreviations: AB, autophagic body; AvP, average precision; GUI, graphical user interface; IoU, intersection over union; MVB, multivesicular body; ROI, region of interest; TEM, transmission electron microscopy; WT,wild type. [ABSTRACT FROM AUTHOR]

Cancer Stem Cells presumably drive tumor growth and resistance to conventional cancer treatments. From a previous computational model, we inferred that these cells are not uniformly distributed in the bulk of a tumorsphere. To confirm this result, we cultivated tumorspheres enriched in stem cells, and performed immunofluorescent detection of the stemness marker SOX2 using confocal microscopy. In this article, we present an image processing method that reconstructs the amount and location of the Cancer Stem Cells in the spheroids. Its advantage is the use of a statistical criterion to classify the cells in Stem and Differentiated, instead of setting an arbitrary threshold. Moreover, the analysis of the experimental images presented in this work agrees with the results from our computational models, thus enforcing the notion that the distribution of Cancer Stem Cells in a tumorsphere is non-homogeneous. Additionally, the method presented here provides a useful tool for analyzing any image in which different kinds of cells are stained with different markers. [ABSTRACT FROM AUTHOR]

The paper deals with a combined time–depth conversion strategy able to improve the reconstruction of voids embedded in an opaque medium, such as cavities, caves, empty hypogeal rooms, and similar targets. The combined time–depth conversion accounts for the propagation velocity of the electromagnetic waves both in free space and in the embedding medium, and it allows better imaging and interpretation of the underground scenario. To assess the strategy's effectiveness, ground penetrating radar (GPR) data referred to as an experimental test in controlled conditions are accounted for and processed by two different approaches to achieve focused images of the scenario under test. The first approach is based on a classical migration algorithm, while the second one faces the imaging as a linear inverse scattering approach. The results corroborate that the combined time–depth conversion improves the imaging in both cases. [ABSTRACT FROM AUTHOR]

The process of image fusion is the process of enriching an image and improving the image's quality, so as to facilitate the subsequent image processing and analysis. With the increasing importance of image fusion technology, the fusion of infrared and visible images has received extensive attention. In today's deep learning environment, deep learning is widely used in the field of image fusion. However, in some applications, it is not possible to obtain a large amount of training data. Because some special organs of snakes can receive and process infrared information and visible information, the fusion method of infrared and visible light to simulate the visual mechanism of snakes came into being. Therefore, this paper takes into account the perspective of visual bionics to achieve image fusion; such methods do not need to obtain a significant amount of training data. However, most of the fusion methods for simulating snakes face the problem of unclear details, so this paper combines this method with a pulse coupled neural network (PCNN). By studying two receptive field models of retinal nerve cells, six dual-mode cell imaging mechanisms of rattlesnakes and their mathematical models and the PCNN model, an improved fusion method of infrared and visible images was proposed. For the proposed fusion method, eleven groups of source images were used, and three non-reference image quality evaluation indexes were compared with seven other fusion methods. The experimental results show that the improved algorithm proposed in this paper is better overall than the comparison method for the three evaluation indexes. [ABSTRACT FROM AUTHOR]

Addressing the challenge of large-scale uneven deformation and the complexities of monitoring road conditions, this study focuses on a segment of the G15 Coastal Highway in Jiangsu Province. It employs PS-InSAR, SBAS-InSAR, and DS-InSAR techniques to comprehensively observe deformation. Analysis of 73 image datasets spanning 2018 to 2021 enables separate derivation of deformation data using distinct InSAR methodologies. Results are then interpreted alongside geological and geomorphological features. Findings indicate widespread deformation along the G15 Coastal Highway, notably significant settlement near Guanyun North Hub and uplift near Guhe Bridge. Maximum deformation rates exceeding 10 mm/year are observed in adjacent areas by all three techniques. To assess data consistency across techniques, identical observation points are identified, and correlation and difference analyses are conducted using statistical software. Results reveal a high correlation between the monitoring outcomes of the three techniques, with an average observation difference of less than 2 mm/year. This underscores the feasibility of employing a combination of these InSAR techniques for road deformation monitoring, offering a reliable approach for establishing real-time monitoring systems and serving as a foundation for ongoing road health assessments. [ABSTRACT FROM AUTHOR]

HER2 expression in breast cancer is evaluated to select patients for anti-HER2 therapy. With the advent of newly approved HER2-targeted drugs for low HER2 expression breast cancer, more solid evidence on the whole spectrum of HER2 expression is needed. In this study, we quantitatively assessed HER2 expression from the whole core by combining high-intensity phosphor-integrated dot (PID) immunostaining and whole slide imaging (WSI) analysis. Two types of staining were performed using a 170-core tissue microarray of invasive breast cancer. First, HER2 was stained by immunohistochemistry (IHC), and IHC scores were determined by two practicing pathologists according to the ASCO/CAP HER2 guideline. Second, HER2 was stained with PID, and tentative PID scores were determined by quantitative analysis. The results show that PID can numerically classify HER2 expression status into scores 3+, 2+, 1+, and 0. The HER2 value quantified by PID strongly correlated with the 3, 3'-diaminobenzidine (DAB) IHC score determined by pathologists (R2 = 0.93). PID IHC score 1+ cases included both DAB IHC score 1+ and 0 cases, and low HER2 expression cases appeared to be often evaluated as DAB IHC score 0. Therefore, digital image analysis by PID and WSI can help stratify HER2 IHC. It may also help classify low HER2 expression. [ABSTRACT FROM AUTHOR]

Change detection, an important task in remote sensing image analysis, has been extensively studied in recent years. However, change detection still faces problems such as difficulty in detecting small targets and incomplete edge detection. Furthermore, pseudo changes such as seasonal changes can also lead to many false detections. In response to these challenges, we propose the Multi-scale Features Mutual Enhancement Network (MFMENet), a simple yet efficient network. MFMENet maximizes feature utilization through mutual guidance and supplementation, enhancing the detection capabilities for small targets and edges. First, we use a lightweight feature extraction network to extract features, which mitigates the information loss caused by continuous downsampling of an overly deep network structure. Then, we design a Context Adaptive Interaction Module (CAIM) to realize the complementarity of feature information at different levels. This facilitates shallow features in gaining more semantic information and deep features in acquiring more texture information, thereby enhancing the model's capability to capture more comprehensive edge features while effectively mitigating interference from pseudo changes. Finally, we introduce a Feature Aggregation Comparison Module (FACM), which uses a combination of aggregation and comparison methods to refine and enhance features. FACM can not only highlight the changed features but also retain more details, improving the model's detection ability of small targets and edge details. The full utilization of features and effective mutual enhancement of information ensure the improvement of MFMENet's performance in small target and edge detection. Extensive experiments on three publicly available datasets (LEVIR, DSIFN, and CDD) demonstrate that our approach achieves superior performance with fewer parameters compared to state-of-the-art methods in recent years. In comparison to these baseline methods, our proposed approach achieves improvements of 0.98%, 12.24%, and 2.03% in the IOU metric on the LEVIR, DSIFN, and CDD datasets, respectively, while utilizing only 1.1 M parameters. [ABSTRACT FROM AUTHOR]

On-shelf availability (OSA) refers to the number of products available in saleable condition to a customer at the place he expects and at the time he wants to buy the product. OSA of the products in retail stores is important to enhance the customer shopping experience and profitability. The products on the retail store shelves are arranged strategically. This arrangement may be changed during shopping, because it is observed that customers may not move the products in the same or at the right place when they pick them up from the shelf. In such real-time scenarios, it would be appropriate to perform shelf image analysis to detect and identify these misplaced objects. This will help the shopkeeper to maintain a large-scale store effortlessly, otherwise it will require massive human effort and labour along with time. Many computer vision-based technology solutions have been developed by researchers to solve this problem. This article discusses a convolutional neural network (CNN)-based method for classifying shelf imagery into the correct semantic classes (that is whether it has misplaced products or not). The proposed architecture was evaluated with a modified COIL-100 dataset. The results of the proposed CNN model were compared with the variant of the proposed model in addition to lightweight pre-trained deep learning models viz; mobilenetV2, densenet121, efficientnetb0, efficientnetb3, and NASNetMobile using transfer learning (TL) concept. According to this study, it has been found that the TL-based MobilenetV2 model is lightweight and achieves better classification performance with 91.28% accuracy. Furthermore, a CNN-based model with 11 user-defined layers provides an accuracy of 90.36%. This shows the efficacy of the proposed models for change detection. [ABSTRACT FROM AUTHOR]

Background: Prompt histopathological diagnosis with accuracy is required for soft tissue sarcomas (STSs) which are still challenging. In addition, the advances in artificial intelligence (AI) along with the development of pathology slides digitization may empower the demand for the prediction of behavior of STSs. In this article, we explored the application of deep learning for prediction of prognosis from histopathological images in patients with STS. Methods: Our retrospective study included a total of 35 histopathological slides from patients with STS. We trained Inception v3 which is proposed method of convolutional neural network based survivability estimation. F1 score which identify the accuracy and area under the receiver operating characteristic curve (AUC) served as main outcome measures from a 4‐fold validation. Results: The cohort included 35 patients with a mean age of 64 years, and the mean follow‐up period was 34 months (2–66 months). Our deep learning method achieved AUC of 0.974 and an accuracy of 91.9% in predicting overall survival. Concerning with the prediction of metastasis‐free survival, the accuracy was 84.2% with the AUC of 0.852. Conclusion: AI might be used to help pathologists with accurate prognosis prediction. This study could substantially improve the clinical management of patients with STS. [ABSTRACT FROM AUTHOR]

Simple Summary: This study shows that automated computer vision techniques are highly effective when used to analyze images and to extract valuable information from them. We trained an algorithm with a set of 1600 images obtained from a study where wildlife approaching wild boar carcasses were monitored. This enabled the model to detect different classes of animals automatically in their natural environment with a mean average precision of 98.11%. Camera traps are becoming widely used for wildlife monitoring and management. However, manual analysis of the resulting image sets is labor-intensive, time-consuming and costly. This study shows that automated computer vision techniques can be extremely helpful in this regard, as they can rapidly and automatically extract valuable information from the images. Specific training with a set of 1600 images obtained from a study where wild animals approaching wild boar carcasses were monitored enabled the model to detect five different classes of animals automatically in their natural environment with a mean average precision of 98.11%, namely 'wild boar', 'fox', 'raccoon dog', 'deer' and 'bird'. In addition, sequences of images were automatically analyzed and the number of wild boar visits and respective group sizes were determined. This study may help to improve and speed up the monitoring of the potential spread of African swine fever virus in areas where wild boar are affected. [ABSTRACT FROM AUTHOR]

To improve the recognition effect of the folk dance image recognition model and put forward new suggestions for teachers’ teaching strategies, this study introduces a Deep Neural Network (DNN) to optimize the folk dance training image recognition model. Moreover, a corresponding teaching strategy optimization scheme is proposed according to the experimental results. Firstly, the image preprocessing and feature extraction of DNN are optimized. Secondly, classification and target detection models are established to analyze the folk dance training images, and the C-dance dataset is used for experiments. Finally, the results are compared with those of the Naive Bayes classifier, K-nearest neighbor, decision tree classifier, support vector machine, and logistic regression models. The results of this study provide new suggestions for teaching strategies. The research results indicate that the optimized classification model shows a significant improvement in classification accuracy across various aspects such as action complexity, dance types, movement speed, dance styles, body dynamics, and rhythm. The accuracy, precision, recall, and F1 scores have increased by approximately 14.7, 11.8, 13.2, and 17.4%, respectively. In the study of factors such as different training images, changes in perspective, lighting conditions, and noise interference, the optimized model demonstrates a substantial enhancement in recognition accuracy and robustness. These findings suggest that, compared to traditional models, the optimized model performs better in identifying various dances and movements, enhancing the accuracy and stability of classification. Based on the experimental results, strategies for optimizing the real-time feedback and assessment mechanism in folk dance teaching, as well as the design of personalized learning paths, are proposed. Therefore, this study holds the potential to be applied in the field of folk dance, promoting the development and innovation of folk dance education. [ABSTRACT FROM AUTHOR]

Advancements in clinical treatment are increasingly constrained by the limitations of supervised learning techniques, which depend heavily on large volumes of annotated data. The annotation process is not only costly but also demands substantial time from clinical specialists. Addressing this issue, we introduce the S4MI (Self-Supervision and Semi-Supervision for Medical Imaging) pipeline, a novel approach that leverages advancements in self-supervised and semi-supervised learning. These techniques engage in auxiliary tasks that do not require labeling, thus simplifying the scaling of machine supervision compared to fully-supervised methods. Our study benchmarks these techniques on three distinct medical imaging datasets to evaluate their effectiveness in classification and segmentation tasks. Notably, we observed that self-supervised learning significantly surpassed the performance of supervised methods in the classification of all evaluated datasets. Remarkably, the semi-supervised approach demonstrated superior outcomes in segmentation, outperforming fully-supervised methods while using 50% fewer labels across all datasets. In line with our commitment to contributing to the scientific community, we have made the S4MI code openly accessible, allowing for broader application and further development of these methods. The code can be accessed at https://github.com/pranavsinghps1/S4MI. [ABSTRACT FROM AUTHOR]

We study the relationship between reflectance and the degree of linear polarization of radiation that bounces off the surface of an unvarnished oil painting. We design a VNIR-SWIR (400 nm to 2500 nm) polarimetric reflectance imaging spectroscopy setup that deploys unpolarized light and allows us to estimate the Stokes vector at the pixel level. We observe a strong negative correlation between the S0 component of the Stokes vector (which can be used to represent the reflectance) and the degree of linear polarization in the visible interval (average -0.81), while the correlation is weaker and varying in the infrared range (average -0.50 in the NIR range between 780 and 1500 nm, and average -0.87 in the SWIR range between 1500 and 2500 nm). By tackling the problem with multi-resolution image analysis, we observe a dependence of the correlation on the local complexity of the surface. Indeed, we observe a general trend that strengthens the negative correlation for the effect of artificial flattening provoked by low image resolutions. [ABSTRACT FROM AUTHOR]

In contemporary times, content-based image retrieval (CBIR) techniques have gained widespread acceptance as a means for end-users to discern and extract specific image content from vast repositories. However, it is noteworthy that a substantial majority of CBIR studies continue to rely on linear methodologies such as gradient-based and derivative-based edge detection techniques. This research explores the integration of bioinspired Spiking Neural Network (SNN) based edge detection within CBIR. We introduce an innovative, computationally efficient SNN-based approach designed explicitly for CBIR applications, outperforming existing SNN models by reducing computational overhead by 2.5 times. The proposed SNN-based edge detection approach is seamlessly incorporated into three distinct CBIR techniques, each employing conventional edge detection methodologies including Sobel, Canny, and image derivatives. Rigorous experimentation and evaluations are carried out utilizing the Corel-10k dataset and crop weed dataset, a widely recognized and frequently adopted benchmark dataset in the realm of image analysis. Importantly, our findings underscore the enhanced performance of CBIR methodologies integrating the proposed SNN-based edge detection approach, with an average increase in mean precision values exceeding 3%. This study conclusively demonstrated the utility of our proposed methodology in optimizing feature extraction, thereby establishing its pivotal role in advancing edge centric CBIR approaches. [ABSTRACT FROM AUTHOR]

Background: Despite major efforts over the last decades, the rising demands of the growing global population makes it of paramount importance to increase crop yields and reduce losses caused by plant pathogens. One way to tackle this is to screen novel resistant genotypes and immunity-inducing agents, which must be conducted in a high-throughput manner. Results: Colour-analyzer is a free web-based tool that can be used to rapidly measure the formation of lesions on leaves. Pixel colour values are often used to distinguish infected from healthy tissues. Some programs employ colour models, such as RGB, HSV or L*a*b*. Colour-analyzer uses two colour models, utilizing both HSV (Hue, Saturation, Value) and L*a*b* values. We found that the a* b* values of the L*a*b* colour model provided the clearest distinction between infected and healthy tissue, while the H and S channels were best to distinguish the leaf area from the background. Conclusion: By combining the a* and b* channels to determine the lesion area, while using the H and S channels to determine the leaf area, Colour-analyzer provides highly accurate information on the size of the lesion as well as the percentage of infected tissue in a high throughput manner and can accelerate the plant immunity research field. [ABSTRACT FROM AUTHOR]