Search Results (1,645)

Cell detection-tracking tasks are vital for biomedical image analysis with potential applications in clinical diagnosis and treatment. However, it poses challenges such as ambiguous boundaries and complex backgrounds in microscopic video sequences, leading to missed detection, false detection, and loss of tracking. Therefore, we propose an enhanced multiple object tracking algorithm IEGS-YOLO + BoT-SORT, named IEGS-BoT, to address these issues. Firstly, the IEGS-YOLO detector is developed for cell detection tasks. It uses the iEMA module, which effectively combines the global information to enhance the local information. Then, we replace the traditional convolutional network in the neck of the YOLO11n with GSConv to reduce the computational complexity while maintaining accuracy. Finally, the BoT-SORT tracker is selected to enhance the accuracy of bounding box positioning through camera motion compensation and Kalman filter. We conduct experiments on the CTMC dataset, and the results show that in the detection phase, the map50 (mean Average Precision) and map50–95 values are 73.2% and 32.6%, outperforming the YOLO11n detector by 1.1% and 0.6%, respectively. In the tracking phase, using the IEGS-BoT method, the multiple objects tracking accuracy (MOTA), higher order tracking accuracy (HOTA), and identification F1 (IDF1) reach 53.97%, 51.30%, and 67.52%, respectively. Compared with the base BoT-SORT, the proposed method achieves improvements of 1.19%, 0.23%, and 1.29% in MOTA, HOTA, and IDF1, respectively. ID switch (IDSW) decreases from 1170 to 894, which demonstrates significant mitigation of identity confusion. This approach effectively addresses the challenges posed by object loss and identity switching in cell tracking, providing a more reliable solution for medical image analysis. Full article

Ventricular arrhythmias such as ventricular fibrillation (VF) and ventricular tachycardia (VT) are among the leading causes of sudden cardiac death worldwide, making their timely and accurate detection a critical task in modern cardiology. This study presents an advanced framework for the automatic detection of critical cardiac arrhythmias—specifically ventricular fibrillation (VF) and ventricular tachycardia (VT)—by integrating deep learning techniques with neuro-fuzzy systems. Electrocardiogram (ECG) signals from the MIT-BIH and AHA databases were preprocessed through denoising, alignment, and segmentation. Convolutional neural networks (CNNs) were employed for deep feature extraction, and the resulting features were used as input for various fuzzy classifiers, including Fuzzy ARTMAP and the Adaptive Neuro-Fuzzy Inference System (ANFIS). Among these classifiers, ANFIS demonstrated the best overall performance. The combination of CNN-based feature extraction with ANFIS yielded the highest classification accuracy across multiple cardiac rhythm types. The classification performance metrics for each rhythm type were as follows: for Normal Sinus Rhythm, precision was 99.09%, sensitivity 98.70%, specificity 98.89%, and F1-score 98.89%. For VF, precision was 95.49%, sensitivity 96.69%, specificity 99.10%, and F1-score 96.09%. For VT, precision was 94.03%, sensitivity 94.26%, specificity 99.54%, and F1-score 94.14%. Finally, for Other Rhythms, precision was 97.74%, sensitivity 97.74%, specificity 99.40%, and F1-score 97.74%. These results demonstrate the strong generalization capability and precision of the proposed architecture, suggesting its potential applicability in real-time biomedical systems such as Automated External Defibrillators (AEDs), Implantable Cardioverter Defibrillators (ICDs), and advanced cardiac monitoring technologies. Full article

Ultrasound imaging is one of the most widespread biomedical imaging techniques thanks to its advantages such as being non-invasive, portable, non-ionizing, and cost-effective. Ultrasound imaging generally provides two-dimensional cross-sectional images, but the quality and interpretative ability vary based on the experience of the examiner, leading to a lack of objectivity and accuracy. To address these issues, there is a growing demand for three-dimensional ultrasound imaging. Among the various types of transducers used to obtain three-dimensional ultrasound images, this paper focuses on the most standardized probe, the linear array transducer, and provides an overview of the system implementations, imaging results, and applications of volumetric ultrasound imaging from the perspective of scanning methods. Through this comprehensive review, future researchers will gain insights into the advantages and disadvantages of various approaches to three-dimensional imaging systems using linear arrays, providing direction and applicability for system configuration and application. Full article

Persistent luminescence nanoparticles (PLNPs) represent a unique class of optical materials. They possess the ability to absorb and store energy from external excitation sources and emit light persistently once excitation terminates. Because of this distinctive property, PLNPs have attracted considerable attention in various areas. Especially in recent years, PLNPs have revealed marked benefits and extensive application potential in fields such as biological detection, imaging, targeted delivery, as well as integrated diagnosis and treatment. Not only do they potently attenuate autofluorescence interference arising from biological tissues, but they also demonstrate superior signal-to-noise ratio and sensitivity in in vivo imaging scenarios. Therefore, regarding the current research, this paper firstly introduces the classification, synthesis methods, and luminescence mechanism of the materials. Subsequently, the research progress of PLNPs in biological detection and imaging and medical treatment in recent years is reviewed. The challenges faced by materials in biomedical applications and the outlook of future development trends are further discussed, which delivers an innovative thought pattern for developing and designing new PLNPs to cater to more practical requirements. Full article

Multimodal imaging techniques have the potential to enhance the interpretation of histology by offering additional molecular and structural information beyond that accessible through hematoxylin and eosin (H&E) staining alone. Here, we present a quantitative approach for comparing the information content of different image modalities, such as H&E and multimodal imaging. We used a combination of deep learning and radiomics-based feature extraction with different information markers, implemented in Python 3.12, to compare the information content of the H&E stain, multimodal imaging, and the combined dataset. We also compared the information content of individual channels in the multimodal image and of different Coherent Anti-Stokes Raman Scattering (CARS) microscopy spectral channels. The quantitative measurements of information that we utilized were Shannon entropy, inverse area under the curve (1-AUC), the number of principal components describing 95% of the variance (PC95), and inverse power law fitting. For example, the combined dataset achieved an entropy value of 0.5740, compared to 0.5310 for H&E and 0.5385 for the multimodal dataset using MobileNetV2 features. The number of principal components required to explain 95 percent of the variance was also highest for the combined dataset, with 62 components, compared to 33 for H&E and 47 for the multimodal dataset. These measurements consistently showed that the combined datasets provide more information. These observations highlight the potential of multimodal combinations to enhance image-based analyses and provide a reproducible framework for comparing imaging approaches in digital pathology and biomedical image analysis. Full article

The detection of aliphatic and aromatic biogenic amines (BAs) is important in food spoilage, environmental monitoring, and disease diagnosis and treatment. Existing fluorescent probes predominantly detect aliphatic BAs with single signal variation and low sensitivity, impairing the adaptability of discriminative sensing platforms. Herein, we present a visual chemosensor (galactose-functionalized pyrrolopyrrole aza-BODIPY, PPAB-Gal) that simultaneously detects eight aliphatic and aromatic BAs in a real-time and intuitive way based on their unique electronic and structural features. Our findings reveal that the dual colorimetric and ratiometric emission changes are rapidly produced in presence of eight BAs through a noncovalent interaction (π–π stacking and hydrogen bond)-assisted chromophore reaction. Specifically, other lone-pair electrons containing compounds, such as secondary amines, tertiary amines, NH₃, and thiol, fail to exhibit these changes. As a result, superior sensing performances with distinctly dual signals (Δλ_ab = 130 nm, Δλ_em = 150 nm), a low LOD (~25 nM), and fast response time (<2 min) were obtained. Based on these advantages, a qualitative and smartphone-assisted sensing platform with a PPAB-Gal-loaded TLC plate is developed for visual detection of putrescine and cadaverine vapor. More importantly, we construct a connection between a standard quantitative index for the TVBN value and fluorescence signals to quantitatively determine the freshness of tuna and shrimp, and the method is facile and convenient for real-time and on-site detection in practical application. Furthermore, since the overexpressed spermine is an important biomarker of cancer diagnosis and treatment, PPAB-Gal NPs can be used to ratiometrically image spermine in living cells. This work provides a promising sensing method for BAs with a novel fluorescent material in food safety fields and biomedical assays. Full article

The precise and efficient sorting of microscopic particles is critical in diverse fields, including biomedical diagnostics, drug development, and environmental monitoring. Fluorescence imaging-activated sorting refers to a strategy where fluorescence images are used to dynamically identify target particles and trigger selective manipulation for sorting purposes. In this study, we introduce a novel microfluidic particle sorting platform that combines optical tweezers with real-time fluorescence imaging for detection. High-speed image analysis enables accurate particle identification and classification, while the optical trap is selectively activated to redirect target particles. To validate the system’s performance, we used 10 µm green and orange fluorescent polystyrene particles. The platform achieved a sorting purity of 94.4% for orange particles under continuous flow conditions. The proposed platform provides an image-based sorting solution, advancing the development of microfluidic systems for high-resolution particle sorting in complex biological and environmental applications. Full article

Nanofluids (NFs), consisting of nanoparticles (NPs) suspended in base fluids, have attracted growing interest due to their superior physicochemical properties and multifunctional potential. In this review, conventional and green NF technology aspects, including synthesis routes, formulation, and applications, are discussed. Conventional NFs, involving NPs synthesized using physical and chemical approaches, have improved NP morphology control but are likely to cause environmental and safety concerns. In contrast, green NFs that are plant extract, microorganism, and biogenic waste-based represent a sustainable and biocompatible alternative. The effect of key parameters (e.g., NP size, shape, concentration, dispersion stability, and base fluid properties) on the performance of NFs is critically examined. The review also covers potential applications: in biomedical engineering (e.g., drug delivery, imaging, theranostics, and antimicrobial therapies), in heat transfer (e.g., solar collectors, cooling electronics, nuclear reactors), and precision machining (e.g., lubricants and coolants). Comparative insights regarding green versus conventionally prepared NFs are provided concerning their toxicity, environmental impact, scalability, and functional performance across various applications. Overall, this review highlights the new promise of both green and conventional NFs and provides key opportunities and challenges to guide future developments in this field. Full article

Accurate measurement of seedling traits is essential for plant phenotyping, particularly in understanding growth dynamics and stress responses. Elm trees (Ulmus spp.), ecologically and economically significant, pose unique challenges due to their curved seedling morphology. Traditional manual measurement methods are time-consuming, prone to human error, and often lack consistency. Moreover, automated approaches remain limited and often fail to accurately process seedlings with nonlinear or curved morphologies. In this study, we introduce GLEN, a deep learning-based model for detecting germinating elm seeds and accurately estimating their lengths of germinating structures. It leverages a dual-path architecture that combines pixel-level spatial features with instance-level semantic information, enabling robust measurement of curved radicles. To support training, we construct GermElmData, a curated dataset of annotated elm seedling images, and introduce a novel synthetic data generation pipeline that produces high-fidelity, morphologically diverse germination images. This reduces the dependence on extensive manual annotations and improves model generalization. Experimental results demonstrate that GLEN achieves an estimation error on the order of millimeters, outperforming existing models. Beyond quantifying germinating elm seeds, the architectural design and data augmentation strategies in GLEN offer a scalable framework for morphological quantification in both plant phenotyping and broader biomedical imaging domains. Full article

Digital holographic microscopy (DHM) provides numerous advantages, such as noninvasive sample analysis, real-time dynamic detection, and three-dimensional (3D) reconstruction, making it a valuable tool in fields such as biomedical research, cell mechanics, and environmental monitoring. To achieve more accurate and comprehensive imaging, it is crucial to capture detailed information on the microstructure and 3D morphology of samples. Phase processing of holograms is essential for recovering phase information, thus making it a core component of DHM. Traditional phase processing techniques often face challenges, such as low accuracy, limited robustness, and poor generalization. Recently, with the ongoing advancements in deep learning, addressing phase processing challenges in DHM has become a key research focus. This paper provides an overview of the principles behind DHM and the characteristics of each phase processing step. It offers a thorough analysis of the progress and challenges of deep learning methods in areas such as phase retrieval, filtering, phase unwrapping, and distortion compensation. The paper concludes by exploring trends, such as ultrafast 3D holographic reconstruction, high-throughput holographic data analysis, multimodal data fusion, and precise quantitative phase analysis. Full article

The Maillard reaction (MR), a non-enzymatic interaction between reducing sugars and amino compounds, plays a pivotal role in developing the flavor, color, and aroma of thermally processed foods. Beyond its culinary relevance, the MR gives rise to a structurally diverse array of compounds, including a novel class of fluorescent nanomaterials known as carbon nanodots (CNDs). These Maillard-derived CNDs, although primarily incidental in food systems, exhibit physicochemical characteristics—such as aqueous solubility, biocompatibility, and tunable fluorescence—that are similar to engineered CNDs currently explored in biomedical fields. While CNDs synthesized through hydrothermal or pyrolytic methods are well-documented for drug delivery and imaging applications, no studies to date have demonstrated the use of Maillard-derived CNDs specifically in drug delivery. This review examines the chemistry of the Maillard reaction, the formation mechanisms and characteristics of food-based CNDs, and their potential functional applications in food safety, bioactivity, and future biomedical use. Additionally, it critically evaluates the health implications of Maillard reaction products (MRPs), including both beneficial antioxidants and harmful by-products such as advanced glycation end-products (AGEs). This integrated perspective highlights the dual role of MR in food quality and human health, while identifying key research gaps needed to harness the full potential of food-origin nanomaterials. Full article

Segmenting cell nuclei in histopathological images is an extremely important process for computational pathology, affecting not only the accuracy of a disease diagnosis but also the analysis of biomarkers and the assessment of cells performed on a large scale. Although many deep learning models can take out global and local features, it is still difficult to find a good balance between semantic context and fine boundary precision, especially when nuclei are overlapping or have changed shapes. In this paper, we put forward a novel deep learning model named Dual-Stream HyperFusionNet (DS-HFN), which is capable of explicitly representing the global contextual and boundary-sensitive features for the robust nuclei segmentation task by first decoupling and then fusing them. The dual-stream encoder in DS-HFN can simultaneously acquire the semantic and edge-focused features, which can be later combined with the help of the attention-driven HyperFeature Embedding Module (HFEM). Additionally, the dual-decoder concept, together with the Gradient-Aligned Loss Function, facilitates structural precision by making the segmentation gradients that are predicted consistent with the ground-truth contours. On various benchmark datasets like TNBC and MoNuSeg, DS-HFN not only achieves better results than other 30 state-of-the-art models in all evaluation metrics but also is less computationally expensive. These findings indicate that DS-HFN provides a capability for accurate nuclei segmentation, which is essential for clinical diagnosis and biomarker analysis, across a wide range of tissues in digital pathology. Full article

Nutrition plays a fundamental role in promoting health and preventing chronic diseases, with bioactive food components offering a therapeutic potential in biomedical applications. Among these, edible oils are recognised for their functional properties, which contribute to disease prevention and metabolic regulation. The proposed study aims to evaluate the quality of four bioactive oils (olive oil, sunflower oil, tomato seed oil, and pumpkin seed oil) by analysing their thermal behaviour through infrared (IR) imaging. The study designed a customised electronic system to acquire thermographic signals under controlled temperature and humidity conditions. The acquisition system was used to extract thermal data. Analysis of the acquired thermal signals revealed characteristic heat absorption profiles used to infer differences in oil properties related to stability and degradation potential. A hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) units was used to classify and differentiate the oils based on stability, thermal reactivity, and potential health benefits. A signal analysis showed that the AI-based method improves both the accuracy (achieving an F1-score of 93.66%) and the repeatability of quality assessments, providing a non-invasive and intelligent framework for the validation and traceability of nutritional compounds. Full article

Background/Objectives: Deep models have achieved remarkable progress in the diagnosis of skin lesions but face two significant drawbacks. First, they cannot effectively explain the basis of their predictions. Although attention visualization tools like Grad-CAM can create heatmaps using deep features, these features often have large receptive fields, resulting in poor spatial alignment with the input image. Second, the design of most deep models neglects interpretable traditional visual features inspired by clinical experience, such as color–texture and edge features. This study aims to propose a novel approach integrating deep learning with traditional visual features to handle these limitations. Methods: We introduce the edge- and color–texture-aware bag-of-local-features model (ECT-BoFM), which limits the receptive field of deep features to a small size and incorporates edge and color–texture information from traditional features. A non-rigid reconstruction strategy ensures that traditional features enhance rather than constrain the model’s performance. Results: Experiments on the ISIC 2018 and 2019 datasets demonstrated that ECT-BoFM yields precise heatmaps and achieves high diagnostic performance, outperforming state-of-the-art methods. Furthermore, training models using only a small number of the most predictive patches identified by ECT-BoFM achieved diagnostic performance comparable to that obtained using full images, demonstrating its efficiency in exploring key clues. Conclusions: ECT-BoFM successfully combines deep learning and traditional visual features, addressing the interpretability and diagnostic accuracy challenges of existing methods. ECT-BoFM provides an interpretable and accurate framework for skin lesion diagnosis, advancing the integration of AI in dermatological research and clinical applications. Full article

Background/Objectives: This paper presents a medical image analysis application designed to facilitate advanced edge detection and fuzzy processing techniques within an intuitive, modular graphical user interface. Methods: Key functionalities include classical edge detection, Ant Colony Optimization (ACO)-based edge extraction, and fuzzy edge generation, which offer improved boundary representation in images where uncertainty and soft transitions are prevalent. Results: One of the main novelties in contrast to the initial innovative Medical Image Analyzer, iMIA, is the fact that the system includes fuzzy C-means clustering to support tissue classification and unsupervised segmentation based on pixel intensity distribution. The application also features an interactive zooming and panning module with the option to overlay edge detection results. As another novelty, fuzzy performance metrics were added, including fuzzy false negatives, fuzzy false positives, fuzzy true positives, and the fuzzy index, offering a more comprehensive and uncertainty-aware evaluation of edge detection accuracy. Conclusions: The application executable file is provided at no cost for the purposes of evaluation and testing. Full article