Search Results (2,181)

Deep learning has achieved remarkable advancements in medical image segmentation, yet its generalization capability across unseen tasks remains a significant challenge. The variety of task objectives, disease-dependent labeling variations, and multi-center data contribute to the high uncertainty of task-specific models on unseen distributions. In this study, we propose PromptSeg, an innovative Transformer-based unified framework for universal 2D medical image segmentation. From an information-theoretic perspective, PromptSeg formulates the segmentation process as a conditional entropy minimization problem, utilizing visual prompts as side information to reduce the uncertainty of the target task. Guided by the information bottleneck principle, PromptSeg aims to utilize the provided visual prompts to filter out redundant noise and learn contextual representations, thereby breaking the restrictions of the task-specific paradigm. When faced with unseen datasets or segmentation targets, our method only requires a few annotated visual prompt pairs to extract task-specific semantics and segment the query images without retraining. Extensive experiments on CT and MRI datasets demonstrate that PromptSeg not only outperforms state-of-the-art methods but also exhibits strong multi-modality generalization capabilities. Full article

Muscle onset detection is a fundamental problem in electromyography signal analysis, human–machine interaction, and rehabilitation assessment. In medical and biomedical applications, slow muscle activation onset processes are widely encountered in scenarios such as rehabilitation training, postural regulation, and fine motor control. Such processes are typically characterized by slowly varying amplitudes, long temporal durations, and high susceptibility to noise interference, which poses significant challenges for accurate identification of onset timing. To address these issues, a lightweight temporal attention method for slow muscle activation onset detection is proposed and systematically validated under multimodal experimental settings. The proposed method takes surface electromyography signals as the primary input, while synchronously acquired optical motion image data are incorporated into the experimental design and result analysis, thereby aligning with the common joint use of optical imaging and physiological signals in medical and biomedical research. From a methodological perspective, the proposed framework is composed of lightweight temporal feature encoding, a slow activation-aware temporal attention mechanism, and noise suppression with stable decision strategies. Under the constraint of low computational complexity, the ability to model progressive activation signals is effectively enhanced. Experiments are conducted on a dataset containing multiple types of slow activation movements, and model performance is evaluated using five-fold cross-validation. The results demonstrate that under regular signal-to-noise ratio conditions, the proposed method significantly outperforms traditional threshold-based approaches, classical machine learning models, and several deep learning baselines in terms of onset detection accuracy, recall, and precision. Specifically, onset detection accuracy reaches approximately $92\%$, recall is around $90\%$, and precision is approximately $93\%$. Meanwhile, the average onset detection error and detection delay are reduced to about $41\mathrm{ms}$ and $28\mathrm{ms}$, respectively, with the false positive rate controlled at approximately $2.2\%$. Stable performance is further maintained under different noise levels and cross-subject settings, indicating strong robustness and generalization capability. Full article

This study proposes a novel liver and liver tumor segmentation model. The architecture integrates BiFormer into the bottom two layers of the Attention U-Net encoder to enhance global semantic context modeling and establish long-range pixel-wise dependencies. The proposed spatial-channel dual attention (SCDA) mechanism is incorporated into the first three encoder layers to refine the fine-grained feature processing capabilities, particularly for precise delineation of liver and tumor boundaries. Eventually, a Mix Structure Block (MSB) is implemented within the decoder to optimize fusion of deep semantic and shallow spatial features, thereby elevating segmentation accuracy. Ablation experiments were conducted on three publicly available datasets. On the 3Dircadb dataset, the mean dice coefficient achieved was 0.9377 and the mean IoU Index achieved was 0.8889. On the LITS dataset, the mean dice coefficient achieved was 0.9257 and the mean IoU Index achieved was 0.8704. On the CHAOS dataset, the mean dice coefficient achieved was 0.9611 and the mean IoU Index achieved was 0.9259. These results validate the functionality and effectiveness of the proposed network model. This study constructed a novel neural network based on attention mechanisms; by enabling precise and automated segmentation directly from raw sensor-acquired medical images, the proposed method enhances the diagnostic value of these imaging sensors, facilitating more accurate clinical decision-making. Full article

The primary objective of this study is to develop a specialized deep learning framework specifically adapted for the unique physical characteristics of neurovascular Optical Coherence Tomography (OCT) imaging. Although Polyp-PVT, originally designed for polyp segmentation, shows promise for OCT analysis, it faces limitations in neurovascular applications. The default RGB input wastes resources on duplicated grayscale data, while its fixed-scale fusion struggles with vascular curvature variations. Furthermore, the attention mechanism fails to capture radial vessel patterns, and geometric constraints limit thin boundary detection. To address these challenges, we propose Brain-OCT-PVT with key innovations: a single-channel input stem reducing parameters by two-thirds; a Radial Intensity Module (RIM) using polar transforms and angular convolution to model annular structures; and a Deformable Cross-scale Fusion Module (D-CFM) with learnable offsets. The Boundary-aware Attention Module (BAM) combines Laplace edge detection with Swin-Transformer for sub-pixel consistency. A specialized loss function combines Dice Similarity Coefficient (Dice), BoundaryIoU on 2-pixel dilated edges, and Focal Tversky to handle extreme class imbalance. Evaluation on 13 clinical cases achieves a Dice score of 95.06% and an 95% Hausdorff Distance (HD95) of 0.269 mm, demonstrating superior performance compared to existing approaches. Full article

Deep learning has been applied in various fields and has been proven to provide good results for classification tasks. However, there is limited understanding of a deep learning model’s decisions, so deep learning is commonly described as a black box. Applying deep learning for critical applications such as medical diagnostic process introduces trust issues. For the deep learning model to be trusted by the medical practitioners, the methods employed by the deep learning model must be seen to be aligned with the diagnostic process employed by the medical practitioners. Explainable methods such as Grad-CAM can be applied to improve the explainability of the deep learning models by providing an visual interpretation of the deep learning classification result decision process. In this study, two deep learning models, VGG16 and ResNet50 are trained using three training methods, one with randomly initialized weights, and two transfer learning methods, which are feature extraction and fine-tuning, to classify the spinal abnormalities based on X-ray images. The classification metrics results are compared and further analyses using Grad-CAM heatmaps are included. The models also evaluated using a stratified five-fold cross-validation, results revealed some disparity between the model’s accuracy and clinical relevance. The randomly initialized VGG16 obtained a classification accuracy of 93.79% but does not focus on clinically relevant regions. On the other hand, not only do the fine-tuned ResNet50 and VGG16 obtain high accuracies of 98.22% and 99.12%, but the heatmaps show that the models focus on more relevant regions. A comparison of the two models shows that the heatmaps produced by the fine-tuned ResNet50 are in more agreement with the clinical view than the fine-tuned VGG16. This study provides a useful reference for interpreting a deep learning-based classification result using explainable method particularly in spine abnormalities analysis with Grad-CAM. Full article

Electrical impedance tomography (EIT) provides noninvasive, high-temporal-resolution imaging for medical and industrial applications. However, accurate image reconstruction remains challenging due to the severe ill-posedness and nonlinearity of the inverse problem, as well as the limited robustness of existing single-source learning-based methods in real measurement scenarios. To address these limitations, a data-constrained and physics-guided Multi-Source Conditional Diffusion Model (MS-CDM) is proposed for EIT image reconstruction. Unlike conventional conditional diffusion methods that rely on a single measurement or an image prior, MS-CDM utilizes boundary voltage measurements as data-driven constraints and incorporates coarse reconstructions as physics-guided structural priors. This multi-source conditioning strategy provides complementary guidance during the reverse diffusion process, enabling balanced recovery of fine boundary details and global topological consistency. To support this framework, a Hybrid Swin–Mamba Denoising U-Net is developed, combining hierarchical window-based self-attention for local spatial modeling with bidirectional state-space modeling for efficient global dependency capture. Extensive experiments on simulated datasets and three real EIT experimental platforms demonstrate that MS-CDM consistently outperforms state-of-the-art numerical, supervised, and diffusion-based methods in terms of reconstruction accuracy, structural consistency, and noise robustness. Moreover, the proposed model exhibits robust cross-system applicability without system-specific retraining under multi-protocol training, highlighting its practical applicability in diverse real-world EIT scenarios. Full article

Cancer is one of the global health problems that affects millions of people every year. Biopsies are among the standard methods for detecting and confirming a cancer diagnosis. Performing this study manually poses several challenges due to tissue movement and the difficulty of precisely locating the target, as is often the case in lung biopsies. This study presents the design and implementation of an autonomous image processing algorithm included in a closed-loop controller that drives the activity of a multi-degree-of-freedom (six) robotic manipulator that performs emulated tissue biopsies. A realistic lung motion emulator, based on a two-degree-of-freedom robotic device with a photon emitter (to simulate radiopharmaceutical identification of cancerous tissue), was used to test the proposed automatic biopsy collector. Applying image processing to detect cancer tissue enables the identification of the centroid and tumor boundaries. Using the detected centroid coordinates, the reference trajectory of the end effector (biopsy needle) was automatically determined. A finite-time convergent controller was implemented to guide the robotic manipulator’s motion towards the tumor position within a specified time window. The controller was evaluated using a digital twin representation of the entire robotic system and using an experimental device working on the simulated mobile tumor emulator. Evaluation of simulated tumor detection and reference trajectory tracking effectiveness was used to validate the operation of the proposed automatic robotic lung biopsy sampler. The application of the controller allows one to track the position of the emulated tumor with a deviation of 0.52 mm and a settling time of less than 1 s. Full article

Injection-related errors remain a common clinical issue and can cause patient discomfort, hematoma formation, and procedural inefficiencies. The visualization of subcutaneous veins using near-infrared (NIR) imaging has gained attention as an effective approach to reducing such errors, as blood exhibits a higher absorption of NIR light than surrounding tissue. In this study, a low-cost, non-invasive vein visualization system is presented to support safer and more accurate venous access. The proposed system integrates an NIR illumination source and a modified webcam within a compact equipment enclosure, allowing subjects to be conveniently examined by placing their arm inside the device. Vein images are automatically acquired using a laptop-based platform, followed by digital image processing techniques for vein enhancement and visualization. Laboratory-scale experiments were conducted on healthy volunteers to evaluate system performance under multiple conditions, including different vein locations (upper and lower arm regions), varying distances between the NIR light source and the arm (15 cm and 20 cm), and ambient illumination interference (light sources on and off). The experimental results demonstrate the successful implementation and reliable operation of the proposed system. Effective vein visualization was achieved across all test conditions, as confirmed by qualitative visual assessment and quantitative image quality metrics, including the Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE). Overall, the proposed system offers a practical, accessible, and cost-effective solution for vein visualization, showing strong potential for clinical and experimental applications aimed at reducing injection errors and improving venous access reliability. Full article

The performance of deep-learning-based segmentation models is strongly dependent on the quality of the input data, which is frequently heterogeneous or degraded in real-world medical imaging scenarios. This work presents a data-centric algorithmic pipeline designed to improve cardiac MRI segmentation accuracy through systematic image enhancement and automatic slice-quality filtering. The proposed method is formalized as deterministic algorithm that combines image processing and supervised learning components. The approach integrates a contrast- and structure-preserving enhancement stage, based on bilateral filtering and adaptive histogram equalization, with a quality-aware selection algorithm. Slice quality is assessed using anatomical attributes extracted via YOLOv11s-based localization and a supervised classification model trained to identify diagnostically reliable images. When applied to transformer-based segmentation architectures such as ViTUNeT, the pipeline yields consistent improvements across all evaluation metrics without increasing model complexity or training cost. These findings emphasize the importance of algorithmic data curation as an effective strategy for enhancing robustness and stability in deep-learning segmentation pipelines and demonstrate the broader applicability of the proposed approach to computer-vision tasks involving heterogeneous or low-quality image datasets. Full article

Purpose: The aim is to evaluate the effectiveness of artificial intelligence (AI)-based automatic segmentation as a predictive tool for clinical peer review in prostate cancer patients treated with hypofractionated radiotherapy. Methodology: A retrospective analysis was conducted on 62 patients treated across three Italian centers between 2020 and 2025. CT images were segmented using software based on 3D U-net models. Three workflows were compared: manual segmentation (C man), automatic segmentation (C AI), and AI-based segmentation adjusted by clinicians (C adj). Quantitative metrics used for comparison included the Dice Similarity Coefficient (DSC) and Hausdorff Distance (HDmax). Statistical analysis involved Welch’s t-test and Cohen’s d for effect size. Results: The results showed a significant improvement in agreement between C AI and C adj compared to C man. Median DSC for CTV increased from 0.80 (C man) to 0.92 (C adj), while HDmax decreased from 12.33 mm to 9.22 mm. Similar improvements were observed for the bladder and anorectum. All differences were statistically significant (p < 0.0001), with large effect sizes (Cohen’s d > 0.8). Discussion: AI use demonstrated a reduction in interobserver variability and segmentation time, enhancing workflow standardization. The C adj workflow, where the physician acts as a reviewer of AI-generated contours, proved effective and potentially integrable into clinical peer review. The predictive peer review refers to a preliminary support step in the clinical review process rather than a substitute for medical decision-making. Full article

Background/Objectives: Pressure injury stage classification was performed using object detection models to address inconsistencies in clinical assessment due to variability in nurses’ experience and education levels. Methods: A dataset of 1282 pressure injury images from a medical institution was used to train and compare five representative architectures, YOLOv5x, YOLOv7, YOLOv8x, YOLOv8n, and YOLOv11x, and Faster R-CNN across Stages 1–4, excluding Deep Tissue Injury and unclassified cases. A mobile application incorporating YOLOv7 was deployed at Eulji University Daejeon Medical Center and tested by 10 nurses over 2 weeks, processing 46 cases. Results: YOLOv7 demonstrated superior performance with mAP@0.5 of 0.97 and mAP@0.5:0.95 of 0.68, achieving 93% accuracy for Stage 2 classification, the most challenging diagnostic category. Clinical validation demonstrated 87% diagnostic accuracy, 4.0/5 user satisfaction, and workflow improvement with assessment time reduced from 4–6 min to 1 min. The application proved valuable as both a diagnostic support tool and educational resource for novice nurses, with zero critical misclassifications recorded. Conclusions: This study establishes the practical utility of AI-based pressure injury classification systems in clinical practice and their potential for enhancing nursing competency and workflow efficiency. Full article

The paper presents a novel method for automated image processing that combines pyramidal generalized contour preprocessing with parallel–hierarchical segmentation, integrating adaptive multilevel thresholding to enhance segmentation accuracy and robustness. The proposed approach is designed to overcome the limitations of traditional methods—whose performance declines under variations in brightness, surface texture, and noise—by enhancing image contrast and structural defect detection, thereby reducing diagnostic errors and misclassification risks. To achieve these objectives, the implementation utilizes multilevel adaptive thresholding, enabling step-by-step segmentation refinement and the extraction of informative regions using three-level coding (positive, negative, and neutral elements). In conjunction with parallel–hierarchical (PH) transformations and high-frequency filtering, the method enhances image contrast, enables more accurate detection of structural defects, and reduces the number of false positives. Experimental results demonstrate a 10–15% improvement in segmentation accuracy compared to classical methods such as region-growing techniques. Furthermore, correlation analysis between automatic and manual segmentation results demonstrated a high degree of consistency, with a correlation coefficient of 0.95–0.99, indicating the reliability and reproducibility of the developed approach. The proposed method is distinguished by its high processing speed, computational simplicity, and versatility of application, ranging from medical thermography for pathological diagnostics to real-time monitoring of railway infrastructure. The practical significance of these results lies in advancing automation, reducing decision-making errors, and ensuring greater reliability of technical and medical control systems. Full article

Super-resolution technology significantly enhances the visual quality of low-resolution medical images, resulting in ultra-high-resolution clear images. Super-resolution technology based on artificial intelligence has achieved great success in reconstruction quality. However, like the image restoration task, super-resolution is also an ill-posed problem, and current work lacks consideration of trustworthiness. Medical image super-resolution needs to ensure clarity and, more importantly, to ensure that the output image is reliable and does not produce false details and mislead the diagnosis. To address the trustworthy issue of medical image super-resolution, we design a novel hybrid loss that combines a hinge-based adversarial term with a PSNR-based regularization. In the designed loss function, the adversarial term makes the reconstructed result close to the distribution of the true high-resolution image, thus generating more refined high-frequency textures, while the PSNR-based regularization term explicitly reduces the deviation from the ground truth. We apply this loss in the global-token U-Net backbone network and add a lightweight VGG as the discriminator for adversarial terms. We empirically verify that integrating the proposed methods can enhance the trustworthiness of medical image super-resolution technology while maintaining high reconstruction quality. Full article

Emerging digital technologies, including immersive environments (VR/AR/XR) and Vision–Language Models (VLMs), have the potential to reshape digital pathology and medical imaging. While immersive tools can enhance spatial visualization and procedural training, VLM-based copilots offer cognitive and workflow support. Their combined impact on cytopathology remains largely conceptual and preclinical. This Conceptual Exploratory Narrative Review (CENR) examines how immersive technologies and VLM-based copilots may jointly influence cytopathologists’ professional workflow, training, and diagnostic processes, introducing the notion of the “augmented cytopathologist.” A structured exploratory approach integrated peer-reviewed literature, position papers, preprints, gray literature (technical reports, white papers, conference abstracts, blogs), and cross-disciplinary perspectives. Database searches (PubMed, Web of Science, Scopus) confirmed a limited number of studies addressing immersive or AI-assisted cytopathology imaging. Thematic analysis focused on four conceptual dimensions: (1) technological capabilities and maturity; (2) workflow and educational applications; (3) professional implications and cytopathologist role; and (4) responsible use of LLMs and VLMs as supportive tools. This approach emphasizes interpretation of emerging trends over aggregation of empirical data, enabling conceptual synthesis of early-stage implementations and perspectives in the field. Immersive technologies facilitate three-dimensional visualization, procedural skill development, and collaborative engagement, whereas VLMs support report generation, literature retrieval, and decision guidance. Together, they offer a synergistic model for perceptual and cognitive augmentation. Key challenges include technical maturity, interoperability, workflow integration, regulatory compliance, and ethical oversight. Figures illustrate representative examples of (1) remote collaborative immersive evaluation and (2) integration of immersive visualization with VLM-based copilots, highlighting potential applications in training and workflow support. The CENR underscores the potential of combining immersive tools and AI copilots to support cytopathology, particularly for education, workflow efficiency, and cognitive augmentation. Adoption should be incremental and carefully governed, emphasizing augmentative rather than transformative use. Future research should focus on clinical validation, scalable integration, and regulatory and ethical frameworks to realize the concept of the augmented cytopathologist in practice. Full article

Automated and comprehensive processing of hyperspectral image data is increasingly important in academic research and medical technology. This study presents an automated processing pipeline that integrates hyperspectral image acquisition, analysis, multimodal fusion, and centralized data management to improve the interpretability of spectral information for biological tissue analysis. The pipeline supports modular hyperspectral data processing, fusion of complementary wavelength ranges, and scalable data storage, and was implemented in Python 3.13.3. The pipeline was evaluated using hyperspectral imaging data acquired from a coronal mouse brain section. Clustering-based analysis and spectral correlation metrics were applied to assess the impact of multimodal data fusion on spectral representation. Clustering of individual modalities yielded silhouette coefficients of 0.5879 for near-infrared data, 0.6020 for mid-infrared data, and 0.6715 for RGB data. Multimodal fusion reduced the silhouette coefficient to 0.5420 and enabled the identification of anatomical structures that were not distinguishable in any single modality. High spectral correlation coefficients exceeding 0.98 confirmed that spectral fidelity was preserved during fusion. These results demonstrate that automated multimodal hyperspectral data fusion can enhance the interpretability of biological tissue despite reduced clustering compactness. The proposed pipeline provides a structured framework for preclinical hyperspectral imaging workflows and supports exploratory biological analysis in medical imaging contexts. Full article