Search Results (379)

Studying zooplankton in freshwater ecosystems is crucial for ecological research, providing insight into ecosystem health, biodiversity, and water quality. This study focuses on developing a hybrid approach for studying and analyzing zooplankton communities using machine learning and human expert analysis. The goal of the study was to automate the labor-intensive process of zooplankton analysis as part of a long-term Lake Baikal monitoring program (since 1945), while maintaining continuity with traditional methods. A software and algorithmic system were developed to automate the analysis: images were processed using a two-stage pipeline (object detection using YOLO V11, classification using metric learning and visual transformers), and complex cases and new species were sent to specialists for verification. Over 240,000 images from 811 samples were processed, and models are updated using verified data to adapt to seasonal changes. An updatable database of labeled zooplankton images suitable for statistical analysis and research has been created. A comparison of manual and machine analysis revealed no significant differences in species composition, with accurate detection in 87% of images. This approach allows for scalable monitoring and the accumulation of labeled data arrays for the development of computer vision methods and the assessment of the state of Lake Baikal’s ecosystem. Full article

Uneven seed spacing, skewed stalk posture, and inconsistent planting depth remain major challenges in horizontal sugarcane planting. To address these issues, a semi-automatic transverse sugarcane planter integrating a supply–buffer–discharge seeder and multiple soil-engaging components was developed. The seed placement process and the interaction between stalk discharge and soil disturbance were investigated through Discrete Element Method (DEM) simulations and experiments. First, the working principle and key component parameters of the whole machine were determined. It integrated the processes of soil crushing, furrowing, seeding, ridge covering. In addition, a dynamic analysis was conducted on the inter-particle disengagement effect during the two-step seed filling process of lifting and discharging. Secondly, a discrete element simulation model for the entire process of soil-engaging seed arrangement operations was established for the machine. The effects of forward speed and seed outlet position were studied using a discrete element method (DEM) simulation model that coupled soil disturbance flow with stalk-seed discharge behaviour. Furthermore, a response surface methodology (RSM) experiment was performed on the seeding test bench to quantify the effects of guiding parameters on seed placement uniformity. The determination coefficient (R²) of the established regression model exceeded 0.9, indicating high prediction accuracy. The optimal collaborative parameter combination was optimized as follows: forward speed of 1.2 m·s⁻¹, buffer inclination angle of 55°and supply roller speed of 26 r·min⁻¹. After verification, the seed placement uniformity coefficient of the seeder reached 91.8 ± 1.4%, which met the expected accuracy requirements for horizontal planting. Full article

Objective: To investigate the integrated relationship between Cerebral Small Vessel Disease (CSVD) markers and quantitative facial soft-tissue measurements in Alzheimer’s disease (AD) continuum, utilizing peripheral muscle health as a potential biomarker for systemic frailty and neurodegeneration. Methods: Retrospective analysis of 3T brain MRI data from 67 patients (AD, N = 45; Mild Cognitive Impairment [MCI], N = 22). CSVD markers were assessed using STRIVE and standardized scales (Fazekas, Potter). Facial soft-tissue metrics, including masseter and tongue volume, temporal muscle thickness (TMT), and fat infiltration (Mercuri Scale), were quantified via semi-automatic segmentation on T1-weighted sequences. Group comparisons (AD vs. MCI) used regression models adjusted for age and sex. The overall central–peripheral relationship was explored via Canonical Correlation Analysis (CCA). Results: The AD group showed a highly significant cognitive decline (MMSE: 23.2 ± 4.1 vs. 28.2 ± 1.4, p < 0.0001). Centrally, the presence of PVSs in the mesencephalic region was the most robust predictor for AD (p = 0.003). Peripherally, average masseter muscle volume was significantly lower in the AD group (p = 0.0273), and masseter fat infiltration was significantly higher (p = 0.025), supporting localized sarcopenia. The CCA demonstrated a statistically significant positive multivariate relationship (r = 0.51, Roy’s Largest Root p = 0.015) between a higher combined CSVD burden and a worse soft tissue profile across the cohort. Conclusions: Quantitative indices of facial soft tissues, particularly masseter muscle volume and quality, reflect systemic frailty and cognitive deterioration along the AD continuum. The strong central–peripheral correlation suggests that sarcopenia and CSVD are interconnected manifestations of a shared pathobiological process. These easily measurable facial markers could serve as valuable, non-invasive peripheral biomarkers, complementing traditional neuroimaging risk stratification in AD. Full article

Posterior acoustic shadowing is a fundamental physical phenomenon associated with calcified stones in ultrasound image, yet it has not been fully exploited in automated ultrasound analysis. This study aimed to develop an explainable, semi-automatic rule-based framework that explicitly incorporates posterior acoustic shadow characteristics for stone detection and localization in a clinically guided manner. A rule-based framework was designed to generate stone candidates using morphological enhancement and to evaluate them through local contrast analysis, posterior shadow region assessment, and shape-based penalties. A composite score integrating these features was used to rank candidates. The method was evaluated on 52 kidney stone and 66 gallbladder stone ultrasound images, stratified into three diagnostic confidence categories. Performance was assessed using an ablation study and centroid distance error measured in pixels relative to expert-defined references. In the 50–60% confidence group, the accuracy increased from 0.29 to 0.64 for kidney stones and from 0.30 to 0.60 for gallbladder stones when posterior shadow information was included. Centroid distance errors in the ≥80% confidence group were 1.26 ± 0.28 mm for kidney stones and 1.44 ± 0.91 mm for gallbladder stones. The proposed framework enhances diagnostic confidence by leveraging physically grounded posterior acoustic shadow analysis and provides a reproducible augmentation to conventional ultrasound-based stone assessment. Full article

Historical remote sensing imagery, including archival aerial photographs and declassified satellite imagery, has been increasingly used to extend earth observation records into periods not covered by modern satellite missions. However, the broader application of these data remains constrained by georeferencing challenges related to incomplete metadata, uncertain acquisition geometry, and heterogeneous image characteristics. This systematic review examines georeferencing practices for historical remote sensing imagery. Out of the 2547 studies identified in the literature, 205 peer-reviewed journal articles were deemed eligible for analysis. This systematic review provides the first comprehensive, PRISMA-compliant synthesis of georeferencing practices for historical remote sensing imagery, analyzing 205 peer-reviewed studies to establish methodological patterns and identify critical gaps. The review considers imagery types, spatial and temporal distributions of case studies, georeferencing workflows, geometric constraints, and accuracy reporting practices. The results indicate a strong reliance on ground control points and a clear preference for manual or semi-automatic georeferencing approaches, while fully automatic methods remain rare. Although the use of historical imagery has increased over time, its potential has not been fully exploited due to persistent georeferencing difficulties, and study areas are often spatially limited or selectively processed to achieve acceptable accuracy. Nevertheless, properly georeferenced historical imagery has been widely applied to land-cover analysis, geomorphology, cryosphere research, hazard assessment, and archeology by extending observation records into earlier decades. Full article

Background/Objectives: The aim of this study was to compare two methods for maxillary sinus volume measurement, assessing their accuracy. The analysis compared the maxillary sinus volume in patients with unilateral cleft lip and palate (UCLP) and in a non-cleft group, using a manual method and a three-dimensional (3D) semi-automated segmentation method. Methods: The research was conducted according to the STROBE guidelines. Sixty patients were included in this study: thirty patients with UCLP were in the research group, and the control group consisted of 30 patients with no craniofacial deformities. Cone-beam computed tomography (CBCT) was analyzed. The manual maxillary sinus volume was calculated based on its approximation to two geometric shapes based on mathematical formulas using linear measurements that were performed on all sinus CBCT scans in the maximum diameter in three planes. The semi-automatic segmentation method using ITK-SNAP 3D-imaging software version 4.2.2 was used to automatically calculate the maxillary sinus volume of the sinuses. The manually calculated volume was compared with the automatically calculated one, and statistical analysis was performed. Results: The cleft group presented lower values in both the automatic and manually calculated volumes for both the right (automatic: p = 0.49; manual p = 0.009) and left (automatic: p = 0.46; manual p = 0.11) maxillary sinuses than the non-cleft group. The cleft group presented statistically significant higher discrepancies in values between the manual and semi-automatic method than the control group (RMSV p = 0.0011; LMSV p = 0.033; TMSV p = 0.003). Conclusions: The manual method may not reveal the exact anatomical topography of the maxillary sinuses. In UCLP patients, the maxillary sinus anatomy may be more complex. Therefore, a semi-automated method may be more advisable to preserve the accuracy of the measurements. Full article

Background/Objectives: To evaluate whether semi-automatic and automatic CT-based liver segmentation methods can provide clinically acceptable volumetric agreement compared with manual segmentation while improving processing efficiency in routine practice. Methods: CT images from 86 individuals were retrospectively analyzed. Liver volumes were calculated using manual segmentation, RVX Semi-Automatic, RVX Deep Learning, and TotalSegmentator. Differences among methods were assessed using repeated-measures ANOVA. Agreement with manual segmentation was evaluated using a Bland–Altman analysis, while the Dice Similarity Coefficient (DICE) and Hausdorff Distance (HD) quantified spatial overlap and boundary deviation, respectively. Processing times were recorded. Results: Mean liver volumes were 1503.9 ± 356.0 cm³ (manual), 1512.6 ± 373.6 cm³ (RVX Semi-Automatic), 1549.8 ± 367.9 cm³ (RVX Deep Learning), and 1518.3 ± 365.8 cm³ (TotalSegmentator). RVX Deep Learning produced significantly higher volumes compared with manual segmentation (p < 0.001), whereas RVX Semi-Automatic and TotalSegmentator showed no significant differences (p > 0.05). DICE values were 0.911 ± 0.032, 0.946 ± 0.018, and 0.938 ± 0.021 for RVX Semi-Automatic, RVX Deep Learning, and TotalSegmentator, respectively. HD values were highest for the deep learning-based method. Processing times were shortest for RVX Deep Learning and longest for manual segmentation. Conclusions: Semi-automatic and automatic liver segmentation methods substantially reduce processing time while maintaining clinically acceptable volumetric agreement. Among the evaluated approaches, TotalSegmentator showed the closest agreement with manual segmentation, supporting its use in routine CT-based liver volumetry. Deep learning-based segmentation, although faster, tended to overestimate volume, potentially limiting its use in applications requiring high volumetric precision. Full article

Background: Radiomic analyses have been extensively explored in oncologic imaging and more recently in neuroimaging. However, radiomic characterization of the crystalline lens using computed tomography has not yet been systematically investigated. Methods: In this retrospective study, semiautomatic segmentation of the eye lens on orbital CT was performed on 112 patients (mean age 48 ± 20 years, 38% female). After radiomics feature extraction, a Boruta feature selection approach based on the random forest algorithm was applied to select the most relevant radiomics features. Severity of white matter lesions were graded according to the Fazekas scale for each patient on axial non-contrast head CT. Results: In total, 17 important features were associated with age-related changes in the eye lens and three important radiomic features for the differentiation between patients with a Fazekas score > 1 and a control group. Significantly higher values were found in patients with a Fazekas score > 1 compared to the control group regarding all three features, “ClusterShade”, “Skewness” and “DifferenceVariance” (p = 0.0006, 0.0023 and 0.0376, respectively), which are all measures of heterogeneity. No important radiomic features of the eye lens were confirmed between patients with and without hypertension. Conclusions: To the best of our knowledge, this is the first study to use CT-based radiomic analysis of the crystalline lens to detect differences among demographic or clinical groups with small vessel disease. The present results might help to expand the range of applications of radiomics regarding ophthalmic (patho-)physiology and suggest a possible new biomarker for systemic vascular diseases. Full article

The syllable is the most fundamental acoustic unit in bird vocalizations and is highly informative of species-specific behavioral characteristics. However, because syllables vary significantly across different species and environments, existing syllable extraction methods still rely on manual or semi-automatic processing, which constrains deep learning-based research on birdsong syllables. This study proposes SFWA-TweetyNet for automatic syllable annotation and applies it to the red-winged blackbird (Agelaius phoeniceus), achieving a validation accuracy of 0.978 and a loss of 0.073. Based on high-quality syllable recognition, this study conducted exploratory cross-regional and cross-seasonal acoustic comparisons at the syllable level to demonstrate a syllable-based analytical framework. Specifically: (1) Acoustic features were extracted from the principal syllables and analyzed using the Kruskal–Wallis test to explore potential variations in acoustic characteristics across regions and seasons; (2) A syllable-based frequency-weighted Acoustic Complexity Index (FW-ACI) was proposed to demonstrate how FW-ACI can be applied for acoustic analysis within the proposed framework, with the Kruskal–Wallis test used as an exploratory statistical tool. In addition, this study constructs a high-quality syllable-level dataset of red-winged blackbird vocalizations, providing important foundational data resources for automatic birdsong annotation, cross-domain soundscape analysis, and avian ecological and behavioral research. Full article

Corneal nerves (CNs) are essential to maintain corneal epithelial integrity and ocular surface homeostasis. In vivo confocal microscopy (IVCM) enables the acquisition of high-resolution visualization of CNs, allowing visualization on a microscopic level. Traditionally, CN images must be analyzed by manual examination, which is time consuming and labor intensive. Artificial intelligence (AI) has facilitated reliable analysis of CN parameters, allowing for automatic and semiautomatic analysis of CNs. These include the identification, segmentation, and quantitative analysis of various CN parameters. This review summarizes the applications of AI-driven, automatic, and semiautomatic models in the CN analysis of IVCM images while also focusing on their diagnostic relevance in dry eye disease (DED) and neuropathic corneal pain (NCP). Recent advancements in AI have transformed IVCM image analysis by improving reproducibility and reducing operator dependency and time. The AI-based algorithm has been demonstrated to have good performance and sensitivity to identify and quantify the CN metrics. AI has also been utilized to improve the diagnostic accuracy of DED with IVCM scans, involving multiple portions of the CNs, such as the inferior whorl region. When employed with IVCM images of patients with NCP, AI-assisted identification of microneuromas and changes in CN metrics has provided an improvement in diagnostic accuracy. Despite promising advances and outcomes, the widespread implementation of these AI models in CN image analysis requires large-scale validation. Future integration of multimodal AI algorithms remains a promising endeavor to enhance diagnostic accuracy and disease stratification. Full article

Background/Objectives: Within human papillomavirus (HPV)-based screening, cytology remains essential for cervical cancer detection while also potentially revealing endometrial pathology. This pilot study aimed to distinguish benign (normal) cases from atypical endometrial hyperplasia (AEH) and endometrial cancer (EC) within atypical glandular cell (AGC) cytology using quantitative analysis of liquid-based cervical cytology. Methods: SurePath and ThinPrep sets included 62 (37 normal, 25 AEH/EC) and 52 (24 normal, 28 AEH/EC) AGC cases, respectively. Semi-automatic QuPath analysis workflow detected cellular clusters; extracted texture, intensity, and geometric features; and produced case-level summaries. A random forest (RF) classifier was used to discriminate AEH/EC from normal cases. Feature subset selection was performed using a beam-search wrapper and joint hyperparameter tuning. Primary performance evaluation comprised stratified 5-fold cross-validation with metrics averaged across these folds. Results: Across both preparations, univariable analyses showed moderate discrimination overall which improved post-menopause. For SurePath and ThinPrep, the highest 10 areas under the curve (AUCs) were 0.701–0.773 (improving to 0.798–0.841 post-menopause) and 0.740–0.778 (improving to 0.832–0.884 post-menopause), respectively. Machine-learning RF models improved performance beyond univariable baselines. Cross-validated AUCs for SurePath and ThinPrep were 0.805 (95% confidence interval [CI], 0.683–0.927) and 0.887 (95% CI, 0.787–0.987), respectively. Features associated with higher AUCs differed between SurePath and ThinPrep, indicating platform-specific signals. Conclusions: Quantitative analysis of routine cervical cytology can augment expert reviews to help distinguish endometrial lesions among AGCs, particularly post-menopause. These software-based readouts can fit within existing workflows and may improve triage when morphology is subtle, including scenarios with HPV-negative screening results. Full article

Background: In forensic ballistics, identifying ammunition types on physical evidence is critical, particularly when metallic residues are minimal. This study comparatively analyzes the elemental signatures deposited by two common projectiles—Full Metal Jacket (FMJ) (Cu/Zn jacket) and Lead Round Nose (LRN) (exposed Pb core)—on complex targets, including pig bone/tissue and mango wood. Methods: Using a semi-automatic handgun at an intermediate range of 5.0 m, residues were examined through high-resolution benchtop Micro-XRF (M4 Tornado) for micro-spatial analysis and Portable XRF (Elio) for rapid field characterization. Additionally, fresh pork leg samples were subjected to a 3-month environmental degradation period to assess trace persistence. Results: Observations indicated that LRN projectiles exhibit markedly elevated Lead (Pb) concentrations along the wound track in bone, hence confirming Pb as a reliable indicator for unjacketed ammunition; specifically, the median Pb concentrations at bullet wiping were 10.39 wt% for M4 and 7.34 wt% for Elio. Conversely, FMJ traces remain strictly confined to the surface bullet wipe area, with median concentrations of Pb, Cu, and Zn being 2.21 wt%, 0.24 wt%, and 0.59 wt% via M4, respectively. Statistical analysis showed a strong correlation for high-concentration elements on tissue, but significantly greater variance on wooden surfaces where FMJ traces exhibited a very weak negative correlation (r = −0.2774) due to minimal and irregular metal transfer. Taphonomic evaluation revealed that the Pb signature from LRN is exceptionally stable (r ≈ 0.9999) even after decomposition, while FMJ signatures are highly sensitive to environmental exposure. Conclusions: This research underscores the necessity of high-sensitivity Micro-XRF (M4) for definitive ammunition verification, providing a refined analytical framework for shooting incident reconstruction even involving degraded remains or complex environmental scenes. Full article

The performances of two autosegmentation algorithms were evaluated on 28 anonymized pelvic CT scans as a pilot study for the clinical implementation of a semi-automatic workflow. Four organs at risk (OARs), namely the rectum, bladder, and femoral heads, were contoured manually by an expert radiation oncologist (RO)—considered as the ground truth (GT)—and by model-based segmentation (MBS) and deep learning (DL) algorithms. Autocontouring performances were evaluated using a qualitative scoring system, contouring time analysis, and five geometrical indices: the 95th percentile Hausdorff Distance (95HD), Dice Similarity Coefficient (DSC), Surface Dice Similarity Coefficient (SDSC), Added Path Length (APL), and Relative Added Path Length (RAPL). Considering total median value for the four OARs, both MBS and DL showed clinically acceptable results with differences between the two algorithms being not statistically significant for almost all indices. The DL autocontouring algorithm achieved high geometric accuracy, high scores from the ROs, and consistent performances with all validation indices for every OAR. The MBS algorithm achieved high geometric accuracy for the femoral heads and bladder. The DL algorithm required 30 s to contour all the OARs, and the MBS algorithm required 90 s, showing a time gain compared with the manual contours, which took 20 min for each case. The DL autocontouring algorithm obtained promising but preliminary results with every evaluation metric and for every analyzed OAR. The application of the MBS algorithm as the only contouring tool still presents challenges. Full article

Particulate Matter (PM) is a type of air pollution that poses risks to human health, the environment, and property. Among the various PM types, PM₁₀ is particularly significant, as it acts as a vector for numerous hazardous trace elements that can negatively impact human health and the ecosystem. Identifying potential sources of PM₁₀ and quantifying their impact on ambient concentrations is crucial for developing efficient control strategies to meet threshold values. Receptor modeling, which identifies sources using chemical species information derived from PM samples, has been widely used for source apportionment. In this study, PM₁₀ samples were collected over three periods (April, May, and June 2021), each lasting 16 days, using semi-automatic dust sampling systems at two sites in Biga, Canakkale, Turkiye. The relative contributions of different source types were quantified using EPA PMF (Positive Matrix Factorization) based on 35 elements comprising PM₁₀. As a result of the analysis, five source types were identified: crustal elements/limestone/calcite quarry (64.9%), coal-fired power plants (11.2%), metal industry (9%), sea salt and ship emissions (8.5%), and road traffic emissions and road dust (6.3%). The distribution of source contributions aligned with the locations of identified sources in the region. Full article

Reservoir bank collapse is a critical geological hazard during the operation of large-scale water conservancy projects, controlled by unique hydrodynamic mechanisms induced by reservoir impoundment, and differs significantly from ordinary landslides. Traditional risk assessment methods, however, often struggle to achieve effective integration between macro-regional zoning and micro-mechanical analysis. Against this limitation, this study proposes a GIS-integrated multi-scale risk screening framework to achieve the preliminary integration of qualitative regional evaluation and quantitative site-specific analysis. Compared with traditional multi-scale studies, the innovations of this research are as follows: (1) a customized GIS component was developed to realize semi-automatic profile extraction from high-resolution DEMs and batch Bishop stability calculations, overcoming the bottleneck of spatializing micro-models over large areas; (2) a “bottom-up” dynamic feedback mechanism was established, utilizing the quantitative safety factor from site-specific evaluations as an explicit indicator for the conservative screening correction of the macro-regional risk map. Applied to the Xiluodu Reservoir, this framework illustrates a potential multi-scale approach for cross-scale risk screening driven by physical–mechanical mechanisms. This provides both a global perspective and a localized physical basis, offering a strategic screening tool for reservoir management. By linking failure mechanisms directly to spatial impacts, the framework provides a plausible conservative feedback rule for risk-informed decision-making in complex reservoir settings. Full article