Segmentation Analysis Research Articles

Advances in Vascular Imaging: A Comparative Analysis of Doppler Ultrasound and Multidetector CT for Lower Limb Peripheral Arterial Disease Diagnosis.

Background This study explores the comparison between Doppler ultrasound and multidetector CT angiography (MDCTA) in diagnosing peripheral arterial disease (PAD), emphasizing the urgent need for precise and minimally invasive methodologies in vascular medicine. PAD, stemming from atherosclerosis, manifests as reduced blood flow and symptoms, such as claudication, requiring timely and accurate diagnosis for optimal treatment outcomes. Doppler ultrasound emerges as an option, offering a non-invasive and cost-effective approach. Conversely, MDCTA provides intricate images, albeit with associated risks, such as radiation exposure and potential complications from contrast agents. This research rigorously evaluates the efficacy, safety, and cost-efficiency of these modalities, aiming to provide clinicians with valuable insights for informed decision-making, ultimately enhancing standards of patient care. Methodology In this prospective study conducted at Saveetha Medical College, Chennai, 34 patients diagnosed with PAD were enrolled to compare the efficacy of duplex ultrasound and MDCTA in identifying arterial lesions. Statistical analysis comprised kappa statistics and contingency tables to evaluate the concordance between the modalities, with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) being calculated. Exclusions were made for patients with contraindications to MDCTA, those under 18 years of age, severe renal impairment, and allergies to contrast agents. This research examined the diagnostic accuracy of both imaging techniques, aiming to provide valuable insights into their effectiveness in identifying arterial lesions associated with PAD. Statistical analysis This investigation studied the efficacy of Doppler ultrasound and MDCTA in diagnosing PAD, with a particular focus on comparing the accuracy of Doppler ultrasonography (DUS) against MDCTA using sensitivity, specificity, and Cohen's kappa coefficient. Through segmental analysis, valuable insights were garnered into the diagnostic precision of DUS across various arterial segments. The results underscored the significance of DUS as a safe, cost-effective, and non-invasive alternative that complements the utility of MDCTA. This comprehensive assessment sheds light on the comparative strengths of both modalities, offering invaluable guidance for clinicians in selecting optimal diagnostic approaches for PAD assessment. Statistical analysis was conducted using the Statistical Package for the Social Sciences (IBM SPSS Statistics for Windows, IBM Corp., Version 24.0, Armonk, NY). Results The sensitivity of ultrasonography (USG) arterial Doppler in evaluating the supra-inguinal, femoropopliteal segments, and infrapopliteal segments was 87.5%, 100%, and 75.32%, respectively. The specificity in evaluating supra-inguinal, femoropopliteal segments, and infrapopliteal segments was 100%, 96.01%, and 83.06%, respectively. The agreement between the two modalities (USG arterial Doppler and CT angiography) obtained by Cohen's kappa analysis with respect to the aortoiliac region and femoropopliteal region was very good (0.91). For infrapopliteal vessels, it was only moderate (0.76). Conclusion Duplex ultrasound emerges as an indispensable tool in the investigation of PAD, offering safety, affordability, and non-invasiveness alongside high diagnostic accuracy and substantial concordance with MDCTA.

Deteksi Tepi Sederhana Pada Citra Menggunakan Operator Sobel

implemented and evaluated the edge detection method using the Sobel Operator, which calculates the gradient of image intensity through two convoluted kernels for horizontal (Gx) and vertical directions. (Gy). The magnitudo gradient is obtained from the combination of both such directional gradients to represent the edge force on each pixel. The main steps include image pre-processing, the application of the Sobel kernel, the calculation of magnitudo gradients, and the filtering of results to extract significant edges. The results show that the Sobel Operator is effective in highlighting intensity differences that indicate the boundary of the object, although it is sensitive to noise and less accurate for fine edges. Despite its limitations, this method is simple to implement and useful as an initial step in image processing applications such as segmentation, pattern identification, and object shape analysis.

Trend Analysis of Climatic Variables in the Cross River Basin, Nigeria

There have been several incidences of flood recently, which are believed to be aggravated by increased climatic variables as a result of perceived changes in climatic conditions (due to climate change) in the Cross River Basin. The basin is the most extensively developed and used river basin in the management of the water resources of the Cross River and Akwa Ibom States in Nigeria. In this paper, 30 years (from 1992 to 2021) of hydro-meteorological data (annual average rainfall, maximum and minimum temperatures, hu midity, duration of sunlight (sunshine hours), evaporation, wind speed, soil temperature, cloud cover, solar radiation, and atmospheric pressure) from four stations in the Cross River Basin were obtained from the Nigerian Meteorological Agency (NIMET), Abuja and subjected to trend detection analysis using the Mann–Kendall test to determine the trend in climatic parameters. The results indicate that there is a significant upward trend in annual rainfall in Ogoja but a downward trend in Calabar. The evaporation trend is significantly downward in Eket, whereas in Calabar, there is an upward trend in solar radiation. Generally, there is a significant rise in annual maximum temperature across the basin. Serial correlation and segmented regression analyses were performed to measure the impact of fluctuations in monthly and long-term Tahiti and Darwin’s Sea level pressures on the climatic variables at the Cross River Basin catchment. These analyses were necessary to determine the extent of the influence of the El Nino Southern Oscillation climatic cycle. The analyses show no significant association between the El Niño Southern Oscillation (ENSO) and rainfall or between the ENSO and runoff in the catchment. This implies that the impact of the ENSO on rainfall and runoff in the Cross River Basin catchment is not considerable. The intercepts derived from the segmented regression in Eket and Ogoja show significant positive trends in both areas for rainfall and runoff. The trends in intercepts suggest that there are external factors influencing rainfall and runoff other than ENSO events, thus strengthening the assertion of climate change. Results from this study will facilitate the understanding of the variability in climatic parameters by stakeholders in the basin, researchers, policymakers, and water resource managers.

GDF-15 is associated with sarcopenia and frailty in acutely admitted older medical patients.

Growth differentiation factor-15 (GDF-15) has been associated with senescence, lower muscle strength, and physical performance in healthy older people. Still, it is not clear whether GDF-15 can be utilized as a biomarker of sarcopenia and frailty in the early stages of hospitalization. We investigated the association of plasma GDF-15 with sarcopenia and frailty in older, acutely admitted medical patients. The present study is based on secondary analyses of cross-sectional data from the Copenhagen PROTECT study, a prospective cohort study including 1071 patients ≥65years of age admitted to the acute medical ward at Copenhagen University Hospital, Bispebjerg, Denmark. Muscle strength was assessed using handgrip strength, and lean mass was assessed using direct segmental multifrequency bioelectrical impedance analyses and used to clarify the potential presence of sarcopenia defined according to guidelines from the European Working Group on Sarcopenia in Older People. Frailty was evaluated using the Clinical Frailty Scale. Plasma GDF-15 was measured using electrochemiluminescence assays from Meso Scale Discovery (MSD, Rockville, MD, USA). We included 1036 patients with completed blood samples (mean age 78.9±7.8years, 53% female). The median concentration of GDF-15 was 2669.3pg/mL. Systemic GDF-15 was significantly higher in patients with either sarcopenia (P<0.01) or frailty (P<0.001) compared with patients without the conditions. Optimum cut-off points of GDF-15 relating to sarcopenia and frailty were 1541 and 2166pg/mL, respectively. Systemic GDF-15 was higher in acutely admitted older medical patients with sarcopenia and frailty compared with patients without. The present study defined the optimum cut-off for GDF-15, related to the presence of sarcopenia and frailty, respectively. When elevated above the derived cutoffs, GDF-15 was strongly associated with frailty and sarcopenia in both crude and fully adjusted models.

Identification and Analysis of Atrial-Bronchial-Abdominal Disharmony in Patients with Isomeric Atrial Appendages.

Ideally, the morphology of atrial appendages should solely be used to identify and differentiate patients with isomeric right and left atrial appendages. However, in clinical practice, the segregation is often indirectly based on the arrangement of thoraco-abdominal structures. The correlation between thoraco-abdominal arrangement and atrial appendages, however, is imperfect. In this study, we sought to clarify the cardiovascular malformations in patients with isomeric atrial appendages with an emphasis on atrial-thoracic-abdominal disharmony. A retrospective review of all patients who underwent cardiac CT angiography between January 2014 and June 2023 and identified to have isomeric atrial appendages was performed. Of the 366 cases (median age: 2years [interquartile range: 11months-7years]), 247 (67.5%) patients had isomeric right atrial appendages while 119 (32.5%) patients had isomeric left atrial appendages. In 316 (86.3%) patients, the thoraco-abdominal arrangement was as per atrial appendage morphology while the remaining 50 (13.6%) patients had disharmonious patterns. Compared to isomeric left atrial appendages, the disharmonious pattern was more frequent with isomeric right atrial appendages (5.9% vs. 17.4%; p 0.003). Irrespective of the type of isomerism, disharmony was mostly confined to the level of the abdomen. Not all patients with isomeric atrial appendages have a harmonious thoraco-abdominal arrangement. The atrial-bronchial-abdominal disharmony is more frequent with isomeric right atrial appendages and is mostly present at the level of the abdomen. A detailed sequential segmental analysis with an independent description of each organ system is, therefore, essential for the complete evaluation of patients with isomeric atrial appendages.

Structural Brain Alterations in Metabolic Syndrome: A Comprehensive MRI Volumetric Analysis of Subcortical and Associated Structures.

Objective: This study aims to elucidate the comprehensive effects of metabolic syndrome (MetS) on the structural integrity of subcortical brain regions and associated structures through high-resolution magnetic resonance imaging (MRI) volumetric analysis, thereby contributing to a deeper understanding of the neuroanatomical dimensions of MetS and its potential implications for cognitive functions and overall brain health. Methods: A cross-sectional design was implemented, involving 25 individuals diagnosed with MetS for at least one year and a healthy control group of 15 individuals at a tertiary hospital's family medicine clinic in Eastern Turkey. Participants underwent a high-resolution MRI scan using a 1.5T Siemens Aera scanner. The MRICloud platform was employed for comprehensive segmentation and quantitative analysis of various brain structures. Results: The study revealed significant volumetric reductions in all measured subcortical brain regions among individuals with MetS compared to the control group (all P < 0.05). Notable differences were observed in key structures such as the substantia nigra, corpus callosum, and thalamus. In subcortical structures, the largest volumetric differences were noted in the basal ganglia L (1322.4 mm³), while the most significant percentage differences were seen in the substantia nigra R (25.24%) and caudate nucleus L (21.02%). Conclusion: The findings from this study underscore the significant neuroanatomical changes associated with MetS, manifesting as volumetric reductions in critical subcortical brain areas. These alterations underscore the necessity for further research into the comprehensive influence of MetS on cognitive processes and the potential for early therapeutic interventions.

Segmentation Analysis of Staycationers in Jakarta

Staycation has been growing and continuously gaining popularity in Indonesia during and after the COVID-19 pandemic. This study aims to identify segments of staycation tourists/staycationers in Jakarta, one of Indonesia's most popular staycation destinations. Using quantitative methods, this study implements a proportional sampling design based on accommodation types with a total of 130 valid and reliable online questionnaire surveys. This study identifies factors and clusters using lifestyle variables from Activity, Interest, and Opinion (AIO) Model and examines their correlation with demographic, geographic, and behaviour segmentation. Data were analysed by descriptive statistics, including factor analysis, cluster analysis, discriminant analysis, and cross-tabulation analysis. The findings revealed four types of staycationers in Jakarta: (1) I-want-everything staycationers, (2) independent and attention seekers, (3) indulgent shopaholics, and (4) culture and local adventurers. Profiles and characters of each staycationer segment were analysed, giving significant knowledge about staycation market, particularly in Jakarta. Thus, this study offers valuable information for tourism and hospitality businesses to focus on the most potential market segment. Keywords: AIO model, lifestyle segmentation, market segmentation, marketing, staycation

SmartEM: machine-learning guided electron microscopy.

Connectomics provides essential nanometer-resolution, synapse-level maps of neural circuits to understand brain activity and behavior. However, few researchers have access to the high-throughput electron microscopes necessary to generate enough data for whole circuit or brain reconstruction. To date, machine-learning methods have been used after the collection of images by electron microscopy (EM) to accelerate and improve neuronal segmentation, synapse reconstruction and other data analysis. With the computational improvements in processing EM images, acquiring EM images has now become the rate-limiting step. Here, in order to speed up EM imaging, we integrate machine-learning into real-time image acquisition in a singlebeam scanning electron microscope. This SmartEM approach allows an electron microscope to perform intelligent, data-aware imaging of specimens. SmartEM allocates the proper imaging time for each region of interest - scanning all pixels equally rapidly, then re-scanning small subareas more slowly where a higher quality signal is required to achieve accurate segmentability, in significantly less time. We demonstrate that this pipeline achieves a 7-fold acceleration of image acquisition time for connectomics using a commercial single-beam SEM. We apply SmartEM to reconstruct a portion of mouse cortex with the same accuracy as traditional microscopy but in less time.

Ticks and lyme disease in natural areas: A segmentation analysis of visitor perceptions of risk and preferred communication strategies

Reported Lyme disease cases have been rapidly increasing globally in recent years. Despite this, few studies have explored how the public perceives health risks associated with ticks and Lyme disease, especially within the context of visitor experiences in natural areas. To address this knowledge gap, we conducted a survey of visitors to one of Ontario, Canada’s most visited provincial parks. Despite high perceptions of risk, few visitors feel sufficiently educated about ticks and Lyme disease. Further, only 51% of visitors would return to the park if future regional incidents of tick-borne illnesses increased by 10–30% (under climate change). Interestingly, no significant correlations were uncovered between home distance and tick-related risk perceptions or self-reported tick education levels. These results may be indicative of the normalization of tick-related risk throughout many of southern Ontario’s most populated regions. Finally, our results also revealed that there is no preferred “one-size-fits-all” risk communication strategy that can be used to support risk-reducing behaviour change in a natural areas context. We recommend a diversity of evidence-based, on-site, and off-site strategies, that can be considered by governing authorities not only in Canada but indeed globally to reduce tick-related health and safety risks and ultimately safeguard visitor experiences in natural areas subject to increasing tick prevalence. Management ImplicationsManagement Implications: The expanding range of ticks and increasing cases of Lyme disease are presenting significant risks to those who visit natural areas, requiring increasing management attention and action by governing authorities, including park managers and regional health units. Through a mixed-methods approach, we examined park visitor perceptions, behaviours, and preferred communication strategies regarding tick and Lyme disease risk. The results revealed a number of key findings, including:•While risk perceptions of tick bites are high, few visitors feel sufficiently educated about ticks and Lyme disease;•Only 51% of visitors would return to the park if future regional incidents of tick-borne illnesses increased by 10–30% (under climate change);•Visitors with further travel distances were significantly less likely to return to the park after experiencing an in-park tick bite; and,•There is no preferred “one-size-fits-all” risk communication strategy within a park visitor context.

Impact of narcotics information management system on inappropriate benzodiazepine receptor agonist prescriptions: A quasi-experimental analysis in South Korea.

The South Korean government implemented the narcotics information management system (NIMS) on 18 May 2018 to manage benzodiazepine receptor agonists (BzRAs) and narcotics effectively and establish a reporting mechanism for these drugs. This study assessed the effects of NIMS on inappropriate use of BzRAs. Using national patient sample data from 2016 to 2020, we analysed adult outpatients who were prescribed oral BzRAs. We conducted a time series and segmented regression analysis using selected indicators to analyse the monthly variations related to the inappropriate use of these medications. The study revealed no significant changes in the indicators of inappropriate BzRA use following the NIMS implementation. Contrary to expectations, there was a significant increase in the proportion of patients exceeding defined daily dose (DDD) and in those receiving concurrent prescriptions of multiple BzRAs, following the implementation of NIMS. The immediate impact of the COVID-19 pandemic was an increase in DDD exceedance; however, overall, this did not significantly affect BzRA use. The introduction of NIMS did not significantly enhance the management of BzRA misuse. Additional measures, including continuous monitoring, system improvements and comprehensive education for prescribers and patients, are recommended to ensure the appropriate use of psychotropic medications.

The U.S. Household Liquid Non-Disinfectant Cleaner Market: A Competitive Profile

This is the nineteenth paper—and twentieth study--that follows the footsteps of nineteen studies that have tried to analyze the competitive profiles of U.S. consumer markets: Men’s Shaving Cream, Beer, Shampoo, Shredded/Grated Cheese, Refrigerated Orange Juice, Men’s Razor-Blades, Women’s Razor-Blades, Toothpaste, Canned Soup, Coffee, Potato Chips, Alkaline AA Battery, Facial Tissue, Toilet Paper, Paper Towel, Disposable Diapers, Sanitary Pads, Automatic-Dishwasher Detergent, and Hand-Dishwashing Detergent. Michael Porter associates high market share with cost leadership strategy, which is based on the idea of competing on a price that is lower than that of the competition.However, customer-perceived quality—not low cost—should be the underpinning of competitive strategy, because it is far more vital to long-term competitive position and profitability than any other factor. So, a superior alternative is to offer better quality vs. the competition.In most consumer markets, a business seeking market share leadership should try to serve the middle class by competing in the mid-price segment; and offering quality better than that of the competition: at a price somewhat higher to signify an image of quality, and to ensure that the strategy is both profitable and sustainable in the long run. The middle class is the socio-economic segment that represents about 40% of households in America.Quality, however, is a complex concept, consumers generally find difficult to understand. So, they often use relative price, and a brand’s reputation, as a symbol of quality.The U.S. Household Liquid Non-Disinfectant Cleaner market had retail sales of $381 million in 2008. It was by far the largest segment of the eleven-segment Household Cleaners market, which had 2008 retail sales of $1,747 million. We have focused our attention on the 24-40 Oz size because it was the most popular, constituting 46% sales of the former market.The Household Liquid Non-Disinfectant Cleaner market was highly competitive, with no dominant player. In 2008 it had 24 brands each with sales over $ 1 million.Using Hierarchical Cluster Analysis, we tested two hypotheses: (I) That the market leader is likely to compete in the mid-price segment, and that (II) Its unit price is likely to be higher than that of the nearest competition.For 2008--and 2007--the data supported both Hypothesis I and II, because the market leader, Formula 409, was a member of the mid-price segment, and its unit price was higher that of the runner-up, Fantastic, also a member of the same segment. We found that relative price was a strategic variable, as we have hypothesized.We also discovered four strategic groups in this market.A pattern is emerging in price-quality segmentation analysis. In thirteen studies—that exclude Men’s Razor-Blades, Women’s Razor-Blades, Coffee, Toilet Paper, Paper Towels, Disposable Diapers, and Sanitary Pads—the market leader was found to be a member of the mid-price segment, as we have hypothesized.Also, results in ten markets supported Hypothesis II.

ESG Perceptions: Investigating Investor Motivations and Characteristics

Integrating ESG factors into investment strategies is a rapidly growing trend, but less is known about how investors value these ESG factors. The characteristics of investors prioritizing ESG in their decisions remain under-recognized. This study uses the Value-Belief-Norm conceptual framework to investigate the relationship between socially responsible motivation and the perceived importance of ESG when making investment decisions. How financial-related, sociodemographic, human capital, and economic variables correlate with the perceived value of ESG was explored. Analyzing data from the 2021 National Financial Capability Study (NFCS) State-by-State and Investor Survey through cumulative logistic regressions, the findings revealed that socially responsible motivation was significantly and positively linked to the likelihood of assigning greater importance to ESG factors. Variables such as objective and subjective financial knowledge and investment experience in years emerged as significant factors. The segment analysis, which was differentiated based on the presence of socially responsible motivation, further highlighted that financial knowledge and investment experience are significantly associated with the importance placed on ESG factors.

Wound Tissue Segmentation and Classification Using U-Net and Random Forest

Analysing wound tissue is a crucial research field for assessing the progression of wound healing. Wounds exhibit certain attributes concerning colour and texture, although these features can vary among different wound images. Research in this field serves multiple purposes, including confirming the presence of chronic wounds, identifying infected wounds, determining the origin of the wound and addressing other factors that classify and characterise various types of wounds. Wounds pose a substantial health concern. Currently, clinicians and nurses mainly evaluate the healing status of wounds based on visual examination. This paper presents an outline of digital image processing and traditional machine learning methods for the tissue analysis of chronic wound images. Here, we propose a novel wound tissue analysis system that consists of wound image pre-processing, wound area segmentation and wound analysis by tissue segmentation. The wound area is extracted using a simple U-Net segmentation model. Granulation, slough and necrotic tissues are the three primary forms of wound tissues. The [Formula: see text]-means clustering technique is employed to assign labels to tissues. Within the wound boundary, the tissue classification is performed by applying the Random Forest classification algorithm. Both segmentation (U-Net) and classification (Random Forest) models are trained, and the segmentation gives 99% accuracy, and the classification model gives 99.21% accuracy.

Survival Dependent on Risk Factors

The Research on risk factor-dependent survival focuses on how an individual's survival can vary depending on the risk factors presented to them. Long-term survival of patients with chronic kidney disease (CKD) is relatively low, especially in those with diabetes. Kidney transplant improves survival. This research is crucial to better understand the relationship between survival and risk factors. This variation in survival depends on gender. The contribution of the research is to carry out a segmented analysis of survival taking into account gender and age. The importance of applying data segmentation using the SPSS package and “in that way” doing a real analysis with respect to various segments such as age and gender is also shown. For age, older adults aged 60 years or older are taken into account.

Biomechanical analysis of adjacent segments after correction surgery for adult idiopathic scoliosis: a finite element analysis

The biomechanical aspects of adjacent segment degeneration after Adult Idiopathic Scoliosis (AdIS) corrective surgery involving postoperative changes in motion and stress of adjacent segments have yet to be investigated. The objective of this study was to evaluate the biomechanical effects of corrective surgery on adjacent segments in adult idiopathic scoliosis by finite element analysis. Based on computed tomography data of the consecutive spine from T1–S1 of a 28-year-old male patient with adult idiopathic scoliosis, a three-dimensional finite element model was established to simulate the biomechanics. Two posterior long-segment fixation and fusion operations were designed: Strategy A, pedicle screws implanted in all segments of both sides, and Strategy B, alternate screws instrumentation on both sides. The range of motion (ROM), Maximum von Mises stress value of intervertebral disc (IVD), and Maximum von Mises stress of the facet joint (FJ) at the fixation adjacent segment were calculated and compared with data of the preoperative AdIS model. Corrective surgery decreased the IVD on the adjacent segments, increased the FJ on the adjacent segments, and decreased the ROM of the adjacent segments. A greater decrease of Maximum von Mises stress was observed on the distal adjacent segment compared with the proximal adjacent segment. The decrease of Maximum von Mises stress and increment of Maximum von Mises stress on adjacent FJ in strategy B was greater than that in strategy A. Under the six operation modes, the change of the Maximum von Mises stress on the adjacent IVD and FJ was significant. The decrease in ROM in the proximal adjacent segment was greater than that of the distal adjacent segment, and the decrease of ROM in strategy A was greater than that in strategy B. This study clarified the biomechanical characteristics of adjacent segments after AdIS corrective surgery, and further biomechanical analysis of two different posterior pedicle screw placement schemes by finite element method. Our study provides a theoretical basis for the pathogenesis, prevention, and treatment of adjacent segment degeneration after corrective surgery for AdIS.

LC-MS/MS quantification of olanzapine in hair after alkaline digestion.

Olanzapine (OLZ), a second-generation antipsychotic drug, is effective in the treatment of acute psychosis, schizophrenia, agitation, bipolar mania, and other psychiatric problems. Antipsychotics are prescribed drugs, which lead the drug abuser to illegal methods of access. This behavior also demonstrates the association of OLZ with criminal involvement, commonly observed at forensic crime scenes. The acute toxicity and even death resulting from OLZ exposure have been highlighted in numerous studies. Therefore, developing analytical techniques to detect OLZ is essential for forensic toxicology. This study aimed to develop a specific and reliable LC-MS/MS method for OLZ detection and quantification in hair samples. The method was validated in terms of selectivity, linearity, limit of detection (LOD), limit of quantification (LOQ), trueness, precision, and uncertainty. The range of linearity was between 0.1-100 ng/mg, with LOD and LOQ values established at 0.036 ng/mg and 0.1ng/mg, respectively. All validation results are within acceptable parameters. The validated method has been applied to authentic hair samples. The variation of OLZ concentrations in 12 hair segments (2 from Case 1 and 10 from Case 2) from two drug-positive patients, ranging from 0.131 to 0.460 ng/mg, is presented in this study. Although several studies have been conducted to determine OLZ in hair samples using segmental analysis via hair solubilization, this study is the first to determine OLZ in hair samples after "digestion" with comparative parameters prior to chromatographic analysis.

Automated intracranial vessel segmentation of 4D flow MRI data in patients with atherosclerotic stenosis using a convolutional neural network.

Intracranial 4D flow MRI enables quantitative assessment of hemodynamics in patients with intracranial atherosclerotic disease (ICAD). However, quantitative assessments are still challenging due to the time-consuming vessel segmentation, especially in the presence of stenoses, which can often result in user variability. To improve the reproducibility and robustness as well as to accelerate data analysis, we developed an accurate, fully automated segmentation for stenosed intracranial vessels using deep learning. 154 dual-VENC 4D flow MRI scans (68 ICAD patients with stenosis, 86 healthy controls) were retrospectively selected. Manual segmentations were used as ground truth for training. For automated segmentation, deep learning was performed using a 3D U-Net. 20 randomly selected cases (10 controls, 10 patients) were separated and solely used for testing. Cross-sectional areas and flow parameters were determined in the Circle of Willis (CoW) and the sinuses. Furthermore, the flow conservation error was calculated. For statistical comparisons, Dice scores (DS), Hausdorff distance (HD), average symmetrical surface distance (ASSD), Bland-Altman analyses, and interclass correlations were computed using the manual segmentations from two independent observers as reference. Finally, three stenosis cases were analyzed in more detail by comparing the 4D flow-based segmentations with segmentations from black blood vessel wall imaging (VWI). Training of the network took approximately 10 h and the average automated segmentation time was 2.2 ± 1.0 s. No significant differences in segmentation performance relative to two independent observers were observed. For the controls, mean DS was 0.85 ± 0.03 for the CoW and 0.86 ± 0.06 for the sinuses. Mean HD was 7.2 ± 1.5 mm (CoW) and 6.6 ± 3.7 mm (sinuses). Mean ASSD was 0.15 ± 0.04 mm (CoW) and 0.22 ± 0.17 mm (sinuses). For the patients, the mean DS was 0.85 ± 0.04 (CoW) and 0.82 ± 0.07 (sinuses), the HD was 8.4 ± 3.1 mm (CoW) and 5.7 ± 1.9 mm (sinuses) and the mean ASSD was 0.22 ± 0.10 mm (CoW) and 0.22 ± 0.11 mm (sinuses). Small bias and limits of agreement were observed in both cohorts for the flow parameters. The assessment of the cross-sectional lumen areas in stenosed vessels revealed very good agreement (ICC: 0.93) with the VWI segmentation but a consistent overestimation (bias ± LOA: 28.1 ± 13.9%). Deep learning was successfully applied for fully automated segmentation of stenosed intracranial vasculatures using 4D flow MRI data. The statistical analysis of segmentation and flow metrics demonstrated very good agreement between the CNN and manual segmentation and good performance in stenosed vessels. To further improve the performance and generalization, more ICAD segmentations as well as other intracranial vascular pathologies will be considered in the future.

Fractional Lévy Stable Motion from a Segmentation Perspective

The segmentation analysis of the Golding–Cox mRNA dataset clarifies the description of these trajectories as a Fractional Lévy Stable Motion (FLSM). The FLSM method has several important advantages. Using only a few parameters, it allows for the detection of jumps in segmented trajectories with non-Gaussian confined parts. The value of each parameter indicates the contribution of confined segments. Non-Gaussian features in mRNA trajectories are attributed to trajectory segmentation. Each segment can be in one of the following diffusion modes: free diffusion, confined motion, and immobility. When free diffusion segments alternate with confined or immobile segments, the mean square displacement of the segmented trajectory resembles subdiffusion. Confined segments have both Gaussian (normal) and non-Gaussian statistics. If random trajectories are estimated as FLSM, they can exhibit either subdiffusion or Lévy diffusion. This approach can be useful for analyzing empirical data with non-Gaussian behavior, and statistical classification of diffusion trajectories helps reveal anomalous dynamics.

Estimation of the time of zolpidem intake and differentiation between consumption and external contamination using MALDI-MSI for investigations on single hair samples

Estimation of drug ingestion time (event time) and distinguishing between drug ingestion and external contamination are important for interpreting hair analysis results in forensics practice. Here, we present a matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) method for in situ analysis of intact hair. We applied a longitudinal cutting method for a single hair to analysis authentic hair samples from a victim of a drug-facilitated sexual assault (DFSA) case and zolpidem-soaked hair. MALDI-MSI showed that zolpidem-positive segments distributed at 4−6 mm or 6−8 mm from the root in three single hairs of a DFSA victim collected 25 days after the event, at concentrations ranging from 0.1 to 5.7 pg mm−1, in agreement with the results from segmental analysis using liquid chromatography tandem mass spectrometry (LC-MS/MS). The estimation of drug intake time was about 20–30 days before sampling, which was consistent with the known time of drug intake. This MALDI-MS method allows imaging analysis of trace substances in a single hair and can realize the intuitive reflection of drug taking time. In addition, zolpidem applied by soaking was mainly distributed on both sides of the longitudinal hair shaft, whereas ingested zolpidem was found only in the middle of the hair shaft of the DFSA victim. The MALDI-MS images of unwashed and washed hair suggested that the amount of externally applied drug was decreased by washing, it was still present on surface layer (cuticle) sides although. Visualization using MALDI-MSI could therefore distinguish between drug ingestion and contamination by reflecting the distribution and deposition site of the drug in hair.

Three-Dimensional X-Ray Computed Tomography Image Segmentation and Point Cloud Reconstruction for Internal Defect Identification in Laser Powder Bed Fused Parts

Abstract Defects shape, volume, and orientation all have a direct impact on the mechanical properties of Laser Powder Bed Fused (L-PBF-ed) parts. Therefore, it is necessary to evaluate and analyze the three-dimensional (3D) geometrical characteristics of these defects. X-ray Computed Tomography (XCT) can reveal an object's internal structure by volumetric scanning through its building direction. Point clouds are 3D data that can be extracted from the stack of XCT images taken from a part to perform further analysis. This study presents a novel approach for 3D segmentation and geometrical analysis of L-PBF defect structures from XCT images. The proposed method integrates Voronoi labeling and 3D point cloud reconstruction to reveal individual defect characteristics from the XCT image stack of a part. A case study showed the proposed methodology's effectiveness in identifying and characterizing defect regions in L-PBF-ed Cobalt-Chrome (CoCr) parts.