Search Results (4,223)

Purpose: To investigate the agreement on perfusion parameters derived from two different commercially available solutions for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in patients with prostate cancer (PCa). Methods: A total of 50 patients (mean age, 71.6; range 56–86) who had undergone radical prostatectomy between December 2021 and September 2022 were included in this retrospective study. All patients had undergone DCE-MRI on a single 3T-MR scanner. Tumor segmentation on MR images was performed by two radiologists in consensus after radiologic-pathologic correlation using topographic maps as a reference standard. Subsequently, four perfusion parameters were calculated by dedicated commercially available solutions from two different vendors. Both solutions adopted a population-based arterial input function and an extended Tofts model as the pharmacokinetic model. The perfusion parameters were as follows; volume transfer constant (K^trans), rate constant (k_ep), volume fraction of extravascular extracellular space (v_e), and volume fraction of plasma (v_p). The differences between paired measurements were compared by Bland–Altman analyses and the reproducibility was evaluated using the intraclass correlation coefficient (ICC). Results: The study population consisted of Gleason score (GS) 6 (n = 12), GS 7 (n = 34), GS 8 (n = 1), and GS 9 (n = 3). Significant differences were found for all parameters (p < 0.0001). Mean differences were as follows: K^trans, −0.2102 (95% confidence interval; −0.2687 to −0.1518); k_ep, −0.7632 (−0.9005 to −0.6258); v_e, −0.1507 (−0.2422 to −0.05907); v_p, −0.02929 (−0.03383 to −0.02476). ICCs for average measures were as follows: K^trans, 0.2989 (−0.2355 to 0.6021); k_ep, 0.6883 (0.4507 to 0.8231); v_e, −0.1331 (−0.9967 to 0.3570); v_p, 0.2653 (−0.3106 to 0.5881). Conclusion: All perfusion parameters were significantly different between the two solutions. Therefore, comparison of perfusion parameters across different solutions is not recommended. Full article

The rapid proliferation of image editing tools poses unprecedented challenges to information sustainability and social trust, as malicious digital forgeries can easily contaminate public discourse, news reporting, and legal forensics. Advanced image editing techniques make image tampering increasingly difficult for the naked eye to recognize, which requires highly accurate methods for detecting and localizing image tampering. In this paper, an end-to-end network model named AEM-Net is proposed. AEM-Net combines RGB and SRM features to enhance the model’s sensitivity to image details and potentially tampered regions through multi-scale feature extraction and fusion. AEM-Net consists of the HRNet-based Multiscale Feature Extraction Module and the Context-Aggregated Pyramid Localization Module (CAPLM). The multi-scale feature extraction module utilizes the Attentional Perceptual Feature Fusion Module to adaptively focus on the anomalous regions. In contrast, the CAPLM utilizes the Expanded Convolutional Feedback Enhancement Module to effectively exploit contextual feature information for achieving pixel-level localization of tampered regions. Experimental results on public benchmark datasets demonstrate that AEM-Net achieves superior performance compared with existing state-of-the-art methods. In particular, AEM-Net achieves an AUC/F1 score of 95.36%/67.19% on CasiaV1, 93.25%/79.75% on Coverage, and 87.36%/66.24% on NIST16, while requiring only 0.09 s to process a single image, demonstrating both high localization accuracy and computational efficiency. Full article

To address the limitations of vision-only defect detection, image–semantic misalignment, and spatial-logic conflicts in complex substation inspection scenarios, this paper proposes a camera-sensor-based multimodal defect sensing framework with cross-modal feature mapping for substation equipment monitoring. The proposed framework integrates field inspection images acquired by camera sensors, defect textual descriptions, and equipment topology knowledge and establishes a unified domain-adaptive pre-training–bidirectional cross-modal mapping–hierarchical reasoning workflow. First, a Contrastive Language–Image Pre-training (CLIP)-based domain-adaptive pre-training strategy is developed to enhance the representation of equipment categories, defect attributes, and inspection-scene semantics. Second, a bidirectional cross-modal feature mapping network is constructed to model fine-grained interactions between candidate visual regions and textual semantics, where uncertainty-aware fusion and prototype constraints are introduced to improve semantic alignment and defect discrimination. Third, a hierarchical neuro-symbolic reasoning module incorporates equipment topology and spatial rules for posterior verification, logical consistency checking, and false-positive suppression. Experiments on a substation inspection image dataset demonstrate that the proposed method achieves 90.8% mAP@0.5, 68.7% mAP@0.5:0.95, and 89.4% F1-score, outperforming mainstream and recent detection models. Full article

Background and Clinical Significance: Left ventricular free-wall rupture (LVFWR) is a rare but devastating mechanical complication of acute myocardial infarction (AMI), with reported in-hospital mortality approaching 90% without surgical intervention. Although its incidence has declined in the contemporary primary percutaneous coronary intervention (PCI) era, LVFWR remains an important cause of early post-infarction death, particularly after delayed reperfusion or fibrinolytic therapy. Subacute or contained “oozing” ruptures pose a unique diagnostic challenge because hemodynamic stability and nonspecific symptoms can mask the underlying catastrophe, and standard transthoracic echocardiography may fail to visualize a sealed defect. Contrast-enhanced cardiac computed tomography (CT) has emerged as a valuable adjunct in this setting, enabling early recognition and surgical planning. Case Presentation: We report a case of a 51-year-old male, a heavy smoker, with acute lateral ST-segment elevation myocardial infarction (STEMI) treated with thrombolysis at a referring hospital, followed by percutaneous coronary intervention (PCI) to the obtuse marginal branch. Despite reperfusion, he developed persistent pleuritic chest pain and a small pericardial effusion. Cardiac computed tomography (CT) demonstrated a contained (sealed) lateral-wall oozing-type left ventricular free-wall rupture (LVFWR) with thrombus sealing the defect. A multidisciplinary heart team initially opted for diligent observation with frequent echocardiography. Within the first 24 h, the pericardial effusion increased, and echocardiography showed circumferential effusion with lateral wall thickening and hematoma, prompting emergent sternotomy. Intraoperatively, a large posterolateral infarct with an oozing-type LV free-wall rupture was identified. Surgical repair was performed using interrupted pledgeted sutures, native pericardial patch, BioGlue, and an overlying Teflon patch, with intra-aortic balloon pump (IABP) support. This case demonstrates the complementary diagnostic value of multimodality imaging—echocardiography for serial monitoring of the pericardial effusion and regional wall changes, and cardiac CT for direct characterization of the contained (sealed) defect—and the timely transition from conservative to surgical management in oozing-type rupture. The patient recovered uneventfully and was discharged in stable condition. Conclusions: This case highlights the diagnostic value of multimodality imaging—particularly cardiac CT—in detecting contained (sealed) LVFWR when echocardiography is inconclusive. Early recognition and prompt surgical intervention enabled a successful outcome in this otherwise frequently fatal complication. Full article

Accurate and robust detection of leaf diseases is a key enabler for precision agriculture and large-scale crop health monitoring. Despite the strong generalization of modern one-stage detectors (e.g., YOLOv8), two domain-specific challenges remain: (i) weak or blurry lesion boundaries hinder precise localization, and (ii) low color contrast between diseased and healthy tissues forces models to rely on subtle texture patterns rather than salient shapes. To tackle these challenges, we reframe the core agricultural disease detection task as the identification of “asymmetric morphological anomalies” and propose a domain-tailored enhancement framework. First, we introduce an Edge Enhancement Module (EEM) that explicitly strengthens boundary-aware representations. Inspired by the natural symmetry of healthy leaves, our EEM is specifically designed to capture symmetry-breaking boundary discontinuities and localized asymmetric edges caused by disease lesions. Our method enhances edge and texture cues that are indicative of disease lesions, which often exhibit local asymmetries and boundary discontinuities. The EEM includes a Differential Normalized Pooling Block (DNPB) that highlights edge responses through discrepancies between max pooling and average pooling, which also models cross-group edge correlations. Second, the Lightweight Texture-Sensitive Feature Enhancement (LTSFE) mechanism amplifies texture-discriminative channels under low-contrast conditions by leveraging complementary global statistics and efficient channel mixing, all with negligible computational overhead. We evaluated our method on a self-constructed dataset of 106,434 images with 225,640 annotations covering diverse crops. Experiments show that the proposed method achieves state-of-the-art accuracy (81.54% mAP@0.5:0.95) while maintaining real-time inference (142 FPS), consistently outperforming strong baselines. Ablations confirm the effectiveness and complementarity of EEM and LTSFE, demonstrating that domain-specific architectural design, inspired by biological symmetry, can substantially improve agricultural vision systems. Full article

Image contrast enhancement is widely used to improve visual perception in digital images; however, it often amplifies noise and introduces artifacts that distort structural information. To address this issue, CLAHE-based contrast enhancement is formulated as a constrained optimization problem, in which distortion control is enforced via PSNR constraints. In this work, a behavioral analysis of the decision variables is conducted, revealing distinct objective-function responses that are exploited to guide the optimization process. Based on these observations, a hybrid evolutionary–numerical framework is developed, combining evolutionary search for discrete parameter exploration with numerical optimization for stable adjustment of continuous parameters. The proposed methods are evaluated on a benchmark set of 30 medical images and compared against fully evolutionary, numerical, and recent population-based optimization approaches reported in the literature. Experimental results show that the hybrid variants, particularly NR-EVO, consistently achieve the best overall performance across different computational budgets, producing higher-quality enhancements for the evaluated benchmark problems. On average, the enhanced images exhibit an increase in entropy of approximately 22% while maintaining competitive structural similarity and satisfying the predefined distortion constraints. Full article

Mining-subsidence-induced surface cracks pose substantial risks to ecological systems, infrastructure stability, and mining safety. Their thin, elongated, discontinuous, and low-contrast characteristics make accurate detection from unmanned aerial vehicle (UAV) imagery challenging, particularly under complex environmental conditions. This study proposes an enhanced YOLO11n framework for detecting surface cracks in mining subsidence areas. Switchable Atrous Convolution (SAConv) was incorporated to strengthen multi-scale feature extraction, while Cascaded Group Attention (CGA) was introduced to suppress background interference and improve feature discrimination, and Shape-IoU loss was adopted to enhance the localization of slender crack targets. The model was evaluated using 5000 annotated UAV images collected in the Zhungeer mining area. It achieved a precision of 85.6%, a recall of 77.9%, an mAP@0.5 of 84.3%, and an F1-score of 81.6%. Compared with the baseline YOLO11n, precision, recall, and mAP@0.5 increased by 1.4, 4.6, and 3.2 percentage points, respectively. Cross-dataset evaluation on the public Crack500 dataset further demonstrated improved robustness under domain variation. These results indicate that the proposed framework improves the detection and localization of slender and discontinuous cracks in complex mining environments, supporting its application in UAV-based geological hazard monitoring. Full article

Accurate tooth segmentation plays a crucial role in computer-aided dental diagnosis and treatment planning, particularly in applications such as tooth detection, lesion localization, orthodontic analysis, and implant surgery. However, panoramic dental X-ray images often suffer from tooth adhesion, low contrast, and blurred boundaries, making precise delineation difficult and potentially compromising downstream clinical analysis. To address these challenges, we propose a boundary-aware segmentation framework, termed Boundary-Aware ResU-Net (BA-ResUNet), which is built upon a ResU-Net backbone and enhanced with Coordinate Attention (CA) and explicit boundary modeling mechanisms. Specifically, CA modules are introduced into the encoder to improve spatial representation and positional awareness. In addition, a Boundary Extraction Module (BEM) is designed to capture boundary priors from shallow and deep features, while a Boundary Injection Module (BIM) progressively incorporates these cues into the decoder through foreground enhancement and background suppression. This design enables the network to better preserve inter-tooth gaps and improve boundary delineation. Experiments on the MICCAI STS-2D dental dataset demonstrate that the proposed method achieves superior performance in terms of Dice and IoU compared with representative existing methods. Ablation and qualitative analyses further show that CA and BEM/BIM play synergistic roles in improving regional overlap and boundary localization, particularly in challenging cases involving adhesion, low contrast, and indistinct contours. These results indicate that the proposed framework provides a reliable and effective solution for panoramic tooth segmentation and has promising potential for computer-aided dental applications. Full article

Transmission electron microscopy (TEM) is a key tool for ultrastructural analysis, yet its performance critically depends on contrast agents, many of which are constrained by toxicity and regulatory concerns. In this study, Wells–Dawson (WD)-type polyoxometalates (POMs), including Parent WD-POM, Lacunary WD-POM, Zr-WD 1:2 POM, and Hf-WD 1:2 POM, were investigated as alternative contrast agents for TEM imaging of rat kidney tissue. Among the tested compounds, Hf-WD 1:2 POM provided the most consistent contrast enhancement, enabling clear visualization of subcellular structures with minimal background interference and no detectable aggregation. Its contrast performance depended on both concentration and incubation time, with higher concentration (0.01 mol/L) and prolonged incubation (24 h) yielding increased signal-to-noise ratio (SNR), although SNR alone did not fully reflect image quality. In comparison with conventional staining agents (uranyl acetate and Uranyless), Hf-WD 1:2 POM achieved significantly improved contrast effectiveness without inducing detectable tissue damage. These findings identify Hf-WD 1:2 POM as a promising non-radioactive alternative for TEM imaging and support the potential of POMs as versatile platforms for the development of advanced contrast agents. Full article

Feature selection represents a critical step in developing accurate and interpretable models for early breast cancer detection. Despite extensive research in the field of mammographic image analysis, no consensus has yet been reached on the optimal feature subsets that distinguish normal from malignant tissues. To address this gap, the present study aims to identify the most discriminative and significant features through a comprehensive multi-criterion selection framework. The aim is to integrate, as new frameworks, different combinations of t-test, ANOVA, Mutual Information (MI), and Equal Grouping Methods (EGM) to rank 19 linear and nonlinear features extracted from mammographic images. The objective is to maximize feature relevance while minimizing redundancy and enhancing diagnostic and healthcare systems. Linear features were assessed alongside nonlinear descriptors. A framework combining t-test, ANOVA, and EGM, guided by MI relevance, was employed to balance feature contributions across categories. The experimental results demonstrated that hybrid feature selection significantly enhanced diagnostic accuracy using optimal linear and nonlinear attributes. The optimization results suggested using a hybrid of six linear and eight nonlinear features. Linear features were highly accurate for detecting cancer. Haralick entropy obtained the highest average accuracy and performance, 94.14% and 93.45%; followed by kurtosis, 93.49% and 92.59%; perimeter irregularity, 93.43% and 92.65%; skewness, 93.01% and 92.25%; and volume/area, 92.82% and 91.92%. Despite the reliable discriminative power of linear descriptors, their overall effectiveness in representing intricate tissue characteristics was limited. The comparison of statistical characteristics shows a distinct performance benefit of nonlinear descriptors over linear ones for detecting breast cancer. Nonlinear descriptors, however, showcased higher accuracy and performance, with an average accuracy of 97.81% in contrast to 94.43% for linear approaches. Local phase congruency achieved the top average accuracy and performance, 97.81% and 96.61%, respectively; succeeded by wavelet entropy, 97.62% and 96.42%; Laplacian spectrum features, 97.52% and 96.32%; nonlinear diffusion, 97.10% and 95.90%; and clustering coefficient, 96.70% and 95.50%; then Shannon, Tsallis, and Rényi entropies. The results indicate that statistically validated nonlinear characteristics significantly outperform linear ones across accuracy and performance measures. Their combination significantly improves the strength and discriminative power of computer-assisted breast cancer diagnostic systems, affirming their suitability for integration into sophisticated machine learning and deep learning models. The results also show that the new multi-criterion framework’s early detection performance surpassed that of the statistical and deep learning models explored, with an average of 98.6% accuracy, 98% sensitivity, 98.9% precision, and 98.4% F1 score of early detection of breast cancer. The incorporation of statistically validated nonlinear descriptors, particularly local phase congruency and wavelet entropy, improves the discriminative ability, robustness, and clinical understanding of breast cancer computer-assisted diagnostic systems. Overall, the proposed framework confirms that integrating hybrid features substantially enhances robustness and plays a pivotal role in computer-assisted breast cancer detection. These selected features may be fed to more advanced algorithms in the future, potentially yielding improved performance. Full article

Background/Objectives: Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide. Early and accurate characterization of suspicious mammographic microcalcifications is essential for improving diagnostic decision-making and reducing unnecessary invasive procedures. Microcalcifications classified as BI-RADS 4 and BI-RADS 5 are clinically important radiological findings; however, differentiating benign from malignant lesions remains challenging because of overlapping morphological and distribution patterns. This study aimed to develop a structured feature-based machine learning model for predicting the pathological diagnosis of breast microcalcifications by integrating mammographic descriptors, patient age, and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) contrast enhancement findings. Methods: The dataset included 53 biopsy-confirmed cases and consisted of clinical and radiological variables, including patient age, calcification morphology, calcification size, distribution pattern, DCE-MRI contrast enhancement status, and histopathological outcome. Several conventional machine learning algorithms were evaluated, including Logistic Regression, Support Vector Machine with radial basis function kernel, K-Nearest Neighbors, Decision Tree, Random Forest, Extra Trees, Gradient Boosting, AdaBoost, and CatBoost. Hyperparameter optimization was performed using grid search with five-fold cross-validation. Model performance was assessed using accuracy, precision, recall, F1-score, ROC-AUC, and log loss. Results: Logistic Regression achieved the highest overall performance, with an accuracy of 0.909 and an F1-score of 0.889, while AdaBoost achieved a recall of 1.000 in the internal evaluation. However, given the limited sample size and lack of external validation, these findings should be interpreted as preliminary. Conclusions: The results suggest that structured radiological descriptors combined with DCE-MRI enhancement information may support malignancy risk stratification of BI-RADS 4–5 microcalcifications, although larger multicenter studies are required before clinical implementation. Full article

Understory vegetation communities in chestnut (Castanea sativa L.) forests feature unique biodiversity patterns and high conservation value, yet the complex drivers of these communities remain poorly quantified. This study investigates the combined effects of structural, microclimatic, and topographic parameters on understory biodiversity in the mountainous region of Chalkidiki, Northern Greece. Using a nested plot design (n = 30), we integrated analytical in situ microclimatic monitoring with hemispherical photography (HemiView canopy image analysis system) to accurately quantify canopy architecture (canopy cover and solar radiation parameters), while a detailed vegetation inventory of vascular plants was performed to determine plant community structure and composition. Generalized Additive Models (GAMs) were employed to model Shannon Diversity (H’) and a weighted rarity index (RSR) representing complementary aspects of understory biodiversity. Our results reveal that the tree slenderness of the dominant stand serves as a robust proxy for stand competition and compactness. Lower slenderness values, reflecting reduced overstory competition, were significantly associated with enhanced light availability and potentially with microclimatic stability, which in turn supported higher levels of species diversity and rarity. Distinct ecological trends were observed between diversity and rarity. Shannon diversity was highest in closed forest environments characterized by lower temperatures, low stand slenderness values, southern aspects, and lower elevations, with the final model explaining 66.1% of the variance (n = 27). In contrast, species rarity was primarily driven by stand slenderness and low disturbance levels (explaining 54.6% of the variance), with the majority of rare species occurring in undisturbed stands (n = 30). These findings suggest that targeted, low-intensity management for competition promotes structurally stable stands and microclimatic buffering, facilitating the preservation of understory biodiversity. Full article

Forestry is recognized as one of the most dangerous industries in the world. To enhance forestry safety, autonomous machinery and safety systems for such machinery are essential. This study aims to introduce large language models (LLMs)—especially their extensions to images, vision–language models (VLMs)—to enable human-like decision-making for autonomous forest machinery. This research focused on VLMs as an active safety system that can adapt to environments and evaluated the effectiveness of a system that quantitatively makes decisions regarding hazard levels using contrastive language–image pretraining (CLIP). The results of industry type, tree state, and road state classification using pretrained models showed that for three tasks—forestry identification, hung-up tree detection, and road collapse sensing—the target classes consistently exhibited higher similarity with disaster texts compared with nontarget classes. Although the F1 scores were 0.693, 0.324 and 0.634, respectively—indicating that the system is insufficient as a direct active safety system—the application of a similarity threshold optimized to maintain a recall of 0.9 yielded F1 scores of 0.291 and 0.584 for tree state and road state, respectively. These results suggest that the system can potentially be used as a quantitative indicator of hazard by setting a threshold on the similarity score. Full article

Reliable delineation of brain tumor boundaries in multimodal magnetic resonance imaging (MRI) remains challenging despite substantial advances in deep learning–based segmentation. Although modern encoder–decoder architectures achieve strong volumetric overlap, precise geometric alignment of tumor contours remains inconsistent, particularly for small lesions and heterogeneous clinical cases. In neuro-oncology, even minor boundary deviations may influence surgical planning, radiotherapy targeting, and longitudinal treatment assessment. These limitations suggest that segmentation performance is not determined solely by network depth or loss design, but also by how multimodal information is structured prior to learning. We introduce CMYD-SurfaceNet, a scale-aware cascaded framework that restructures multimodal MRI inputs at the representation level to enhance boundary-sensitive segmentation. Rather than treating modalities as independently concatenated channels, selected sequences are first organized into a task-guided pseudo-RGB projection. This intermediate representation is subsequently transformed into the CMYK color space to disentangle shared luminance structure from modality-specific contrast dominance. To further encode geometric priors, a gradient-derived boundary density channel is incorporated to explicitly emphasize spatial discontinuities corresponding to tumor margins. The resulting CMYD representation is integrated within a two-stage nnU-Net cascade, where global tumor localization is followed by high-resolution region-of-interest refinement with auxiliary contour supervision. This scale-aware design improves sensitivity to small tumor components while stabilizing contour delineation. Extensive evaluation on the BraTS benchmark demonstrates consistent improvements in boundary-sensitive metrics. Compared with baseline nnU-Net, the proposed framework reduces HD95 from 3.6 mm to 2.4 mm and increases Surface Dice at 1 mm tolerance from 0.82 to 0.89, while maintaining competitive Dice performance. These findings suggest that representation-level structural decoupling, when combined with scale-aware refinement, may provide clinically relevant boundary-aware multimodal MRI segmentation support without increasing architectural complexity. Full article

Accurate identification of pathogenic yeasts is essential for clinical diagnosis and effective antifungal therapy. However, current approaches predominantly rely on microscopy-based models, which require large-scale annotated datasets and exhibit limited generalization across morphologically similar species. In contrast, light-scattering (LS) imaging captures the diffraction patterns generated by internal cellular structures, providing volumetric biophysical cues that extend beyond surface morphology, yet its indirect representations pose major challenges for feature discrimination. Our objective is to develop fast and accurate methods to detect various species of yeasts. We propose FPA-YeastNet, which is a frequency-enhanced single-modality deep learning architecture that improves yeast classification in LS images by leveraging discriminative frequency-domain features. Building upon this enhanced modality, we further propose FGCA-YeastNet, a frequency-guided cross-attention network designed to integrate LS and microscopy information for complementary representation learning. The proposed multimodal model facilitates synergistic interactions between volumetric scattering structures and fine-grained cellular textures through adaptive fusion and bidirectional attention, leading to improved robustness and interpretability. Comprehensive classification experiments conducted on a multimodal yeast dataset demonstrate that FGCA-YeastNet effectively bridges the performance gap between LS and microscopy modalities, achieving significant improvements over both unimodal and multimodal baselines. The FPA-YeastNet yields an average accuracy improvement of 6.26% compared with LS-only models, and FGCA-YeastNet further provides mean gains of 19.97% and 7.67% over unimodal and multimodal baseline models, respectively. Experimental results demonstrate the diagnostic potential of light scattering and microscopic imaging and underscore the effectiveness of frequency-guided multimodal collaboration for reliable and interpretable yeast classification in clinical microbiology. Full article