Journal of Imaging

Transverse fetal head position during labor is associated with increased rates of operative deliveries and cesarean sections. Traditional assessment methods rely on digital examination, which can be inaccurate in cases of prolonged labor. Intrapartum ultrasound offers improved diagnostic capabilities, but standardized interpretation frameworks are needed. This study aimed to evaluate the significance of appropriate assessment and management of transverse fetal head position during labor, with particular emphasis on the correlation between geometric parameters and delivery outcomes. Additionally, the investigation analyzed the potential role of Artificial Intelligence Dystocia Algorithm (AIDA) as an innovative decision support system in standardizing diagnostic approaches and optimizing clinical decision-making in cases of fetal malposition. This investigation was conducted as a focused secondary analysis of data originally collected for the development and validation of the Artificial Intelligence Dystocia Algorithm (AIDA). The study examined 66 cases of transverse fetal head position from a cohort of 135 nulliparous women with prolonged second-stage labor across three Italian hospitals. Cases were stratified by Midline Angle (MLA) measurements into classic transverse (≥75°), near-transverse (70–74°), and transitional (60–69°) positions. Four geometric parameters (Angle of Progression, Head–Symphysis Distance, Midline Angle, and Asynclitism Degree) were evaluated using the AIDA classification system. The predictive capabilities of three machine learning algorithms (Support Vector Machine, Random Forest, and Multilayer Perceptron) were assessed, and delivery outcomes were analyzed. The AIDA system successfully categorized labor dystocia into five distinct classes, with strong predictive value for delivery outcomes. A clear gradient of cesarean delivery risk was observed across the spectrum of transverse positions (100%, 93.1%, and 85.7% for near-transverse, classic transverse, and transitional positions, respectively). All cases classified as AIDA Class 4 required cesarean delivery regardless of the specific MLA value. Machine learning algorithms demonstrated high predictive accuracy, with Random Forest achieving 95.5% overall accuracy across the study cohort. The presence of concurrent asynclitism with transverse position was associated with particularly high rates of cesarean delivery. Among the seven cases that achieved vaginal delivery despite transverse positioning, none belonged to the classic transverse positions group, and five (71.4%) exhibited at least one parameter classified as favorable. The integration of artificial intelligence through AIDA as a decision support system, combined with intrapartum ultrasound, offered a promising approach for objective assessment and management of transverse fetal head position. The AIDA classification system’s integration of multiple geometric parameters, with particular emphasis on precise Midline Angle (MLA) measurement in degrees, provided superior predictive capability for delivery outcomes compared to qualitative position assessment alone. This multidimensional approach enabled more personalized and evidence-based management of malpositions during labor, potentially reducing unnecessary interventions while identifying cases where expectant management might be futile. Further prospective studies are needed to validate the predictive capability of this decision support system and its impact on clinical decision-making in real-time labor management. Full article

Thoracic injuries account for a high percentage of combat casualty mortalities, with 80% of preventable deaths resulting from abdominal or thoracic hemorrhage. An effective method for detecting and triaging thoracic injuries is point-of-care ultrasound (POCUS), as it is a cheap and portable noninvasive imaging method. POCUS image interpretation of pneumothorax (PTX) or hemothorax (HTX) injuries requires a skilled radiologist, which will likely not be available in austere situations where injury detection and triage are most critical. With the recent growth in artificial intelligence (AI) for healthcare, the hypothesis for this study is that deep learning (DL) models for classifying images as showing HTX or PTX injury, or being negative for injury can be developed for lowering the skill threshold for POCUS diagnostics on the future battlefield. Three-class deep learning classification AI models were developed using a motion-mode ultrasound dataset captured in animal study experiments from more than 25 swine subjects. Cluster analysis was used to define the “population” based on brightness, contrast, and kurtosis properties. A MobileNetV3 DL model architecture was tuned across a variety of hyperparameters, with the results ultimately being evaluated using images captured in real-time. Different hyperparameter configurations were blind-tested, resulting in models trained on filtered data having a real-time accuracy from 89 to 96%, as opposed to 78–95% when trained without filtering and optimization. The best model achieved a blind accuracy of 85% when inferencing on data collected in real-time, surpassing previous YOLOv8 models by 17%. AI models can be developed that are suitable for high performance in real-time for thoracic injury determination and are suitable for potentially addressing challenges with responding to emergency casualty situations and reducing the skill threshold for using and interpreting POCUS. Full article

This meta-analysis evaluated the diagnostic accuracy of sonoelastography for distinguishing benign and malignant breast lesions, comparing strain elastography and shear wave elastography (SWE). We systematically reviewed 825 publications, selecting 30 studies (6200 lesions: 45% benign, 55% malignant). The pooled sensitivity and specificity for overall sonoelastography were 88% (95% CI: 85–91%) and 84% (95% CI: 81–87%), respectively. Strain elastography showed sensitivity and specificity of 85% and 80%, respectively, while SWE demonstrated superior performance with 90% sensitivity, 86% specificity, and an AUC of 0.92. Moderate heterogeneity (I² = 55%) was attributed to study variation. SWE showed the potential to reduce unnecessary biopsies by 30–40% by increasing specificity. AI-assisted image analysis and standardized protocols may enhance accuracy and reduce variability. These findings support the integration of SWE into breast imaging protocols. Full article

This study presents a novel framework that integrates Vision Graph Neural Networks (ViGs) with supervised contrastive learning for enhanced spectro-temporal image analysis of speech signals in Parkinson’s disease (PD) detection. The approach introduces a frequency band decomposition strategy that transforms raw audio into three complementary spectral representations, capturing distinct PD-specific characteristics across low-frequency (0–2 kHz), mid-frequency (2–6 kHz), and high-frequency (6 kHz+) bands. The framework processes mel multi-band spectro-temporal representations through a ViG architecture that models complex graph-based relationships between spectral and temporal components, trained using a supervised contrastive objective that learns discriminative representations distinguishing PD-affected from healthy speech patterns. Comprehensive experimental validation on multi-institutional datasets from Italy, Colombia, and Spain demonstrates that the proposed ViG-contrastive framework achieves superior classification performance, with the ViG-M-GELU architecture achieving 91.78% test accuracy. The integration of graph neural networks with contrastive learning enables effective learning from limited labeled data while capturing complex spectro-temporal relationships that traditional Convolution Neural Network (CNN) approaches miss, representing a promising direction for developing more accurate and clinically viable speech-based diagnostic tools for PD. Full article

Microscopic cell classification is a fundamental challenge in both clinical diagnosis and biological research. However, existing methods still struggle with the complexity and morphological diversity of cellular images, leading to limited accuracy or high computational costs. To overcome these constraints, we propose an efficient classification method that balances strong feature representation with a lightweight design. Specifically, an Inception-Linear Attention-based Lightweight Vision Transformer (ILViT) model is developed for microscopic cell classification. The ILViT integrates two innovative modules: Dynamic Inception Convolution (DIC) and Contrastive Omni-Kolmogorov Attention (COKA). DIC combines dynamic and Inception-style convolutions to replace large kernels with fewer parameters. COKA integrates Omni-Dimensional Dynamic Convolution (ODC), linear attention, and a Kolmogorov-Arnold Network(KAN) structure to enhance feature learning and model interpretability. With only 1.91 GFLOPs and 8.98 million parameters, ILViT achieves high efficiency. Extensive experiments on four public datasets are conducted to validate the effectiveness of the proposed method. It achieves an accuracy of 97.185% on BioMediTech dataset for classifying retinal pigment epithelial cells, 97.436% on ICPR-HEp-2 dataset for diagnosing autoimmune disorders via HEp-2 cell classification, 90.528% on Hematological Malignancy Bone Marrow Cytology Expert Annotation dataset for categorizing bone marrow cells, and 99.758% on a white blood cell dataset for distinguishing leukocyte subtypes. These results show that ILViT outperforms the state-of-the-art models in both accuracy and efficiency, demonstrating strong generalizability and practical potential for cell image classification. Full article

Self-supervised depth estimation from monocular image sequences provides depth information without costly sensors like LiDAR, offering significant value for autonomous driving. Although self-supervised algorithms can reduce the dependence on labeled data, the performance is still affected by scene occlusions, lighting differences, and sparse textures. Existing methods do not consider the enhancement and interaction fusion of features. In this paper, we propose a novel parallel multi-scale semantic-depth interactive fusion network. First, we adopt a multi-stage feature attention network for feature extraction, and a parallel semantic-depth interactive fusion module is introduced to refine edges. Furthermore, we also employ a metric loss based on semantic edges to take full advantage of semantic geometric information. Our network is trained and evaluated on KITTI datasets. The experimental results show that the methods achieve satisfactory performance compared to other existing methods. Full article

Soft tissue masses are predominantly benign, with a benign-to-malignant ratio exceeding 100:1, often located around joints. They may be contiguous or adjacent to joints or reflect systemic diseases or distant organ involvement. Clinically, they typically present as palpable swellings. Evaluation should consider duration, size, depth, and mobility. Also assess consistency, growth rate, symptoms, and history of trauma, infection, or malignancy. Laboratory tests are generally of limited diagnostic value. The primary clinical goal is to avoid unnecessary investigations or procedures for benign lesions while ensuring timely diagnosis and treatment of malignant ones. Imaging plays a central role: it confirms the presence of the lesion, assesses its location, size, and composition, differentiates between cystic and solid or benign and malignant features, and can sometimes provide a definitive diagnosis. Imaging is also crucial for biopsy planning, treatment strategy, identification of involved structures, and follow-up. Ultrasound (US) is the first-line imaging modality for palpable soft tissue masses due to its low cost, wide availability, and lack of ionizing radiation. If findings are inconclusive, magnetic resonance imaging (MRI) or computed tomography (CT) is recommended. This review aims to discuss the most common causes of periarticular soft tissue masses in the appendicular skeleton, focusing on clinical presentation and radiologic features. Full article

Meningiomas are the most common primary intracranial tumors in adults. For most cases, surgical resection is effective in mitigating recurrence risk. Accurate visualization of meningiomas helps radiologists assess the distribution and volume of the tumor within the brain while assisting neurosurgeons in preoperative planning. This paper introduces an innovative realistic 3D medical visualization system, namely SegR3D. It incorporates a 3D medical image segmentation pipeline, which preprocesses the data via semi-supervised learning-based multi-target segmentation to generate masks of the lesion areas. Subsequently, both the original medical images and segmentation masks are utilized as non-scalar volume data inputs into the realistic rendering pipeline. We propose a novel importance transfer function, assigning varying degrees of importance to different mask values to emphasize the areas of interest. Our rendering pipeline integrates physically based rendering with advanced illumination techniques to enhance the depiction of the structural characteristics and shapes of lesion areas. We conducted a user study involving medical practitioners to evaluate the effectiveness of SegR3D. Our experimental results indicate that SegR3D demonstrates superior efficacy in the visual analysis of meningiomas compared to conventional visualization methods. Full article

Two-dimensional imaging is still commonly used in dentistry, but does not provide the three-dimensional information often required for the accurate assessment of dental structures. Photon-counting computed tomography (PCCT), a new three-dimensional modality mainly used in general medicine, has shown promising potential for dental applications. With growing digitalization and cross-disciplinary integration, using PCCT data from other medical fields is becoming increasingly relevant. Conventional CT scans, such as those of the cervical spine, have so far lacked the resolution to reliably evaluate dental structures or implants. This study evaluates the diagnostic utility of PCCT for visualizing peri-implant structures in routine clinical photon-counting CT acquisitions and assesses the influence of metal artifact reduction (MAR) algorithms on image quality. Ten dental implants were retrospectively included in this IRB-approved study. Standard PCCT scans were reconstructed at multiple keV levels with and without MAR. Quantitative image analysis was performed with respect to contrast and image noise. Qualitative evaluation of peri-implant tissues, implant shoulder, and apex was performed independently by two experienced dental professionals using a five-point Likert scale. Inter-reader agreement was measured using intraclass correlation coefficients (ICCs). PCCT enabled high-resolution imaging of all peri-implant regions with excellent inter-reader agreement (ICC > 0.75 for all structures). Non-MAR reconstructions consistently outperformed MAR reconstructions across all evaluated regions. MAR led to reduced clarity, particularly in immediate peri-implant areas, without significant benefit from energy level adjustments. All imaging protocols were deemed diagnostically acceptable. This is the first in vivo study demonstrating the feasibility of opportunistic dental diagnostics using PCCT in a clinical setting. While MAR reduces peripheral artifacts, it adversely affects image clarity near implants. PCCT offers excellent image quality for peri-implant assessments and enables incidental detection of dental pathologies without additional radiation exposure. PCCT opens new possibilities for opportunistic, three-dimensional dental diagnostics during non-dental CT scans, potentially enabling earlier detection of clinically significant pathologies. Full article

Background:Generative Adversarial Networks (GANs), thanks to their great versatility, have a plethora of applications in biomedical imaging with the goal of simulating complex pathological conditions and creating clinical data used for training advanced machine learning models. The ability to generate high-quality synthetic clinical data not only addresses issues related to the scarcity of annotated bioimages but also supports the continuous improvement of diagnostic tools. Method: We propose a two-step method aimed to detect whether a bioimage can be considered fake or real. The first step is related to bioimage generation using a Deep Convolutional GAN, while the second step involves the training and testing of a set of machine learning models aimed to distinguish between real and generated bioimages. Results: We evaluate our approach by exploiting six different datasets. We observe notable results, demonstrating the ability of Deep Convolutional GAN to generate realistic synthetic images for some specific bioimages. However, for other bioimages, the accuracy does not align with the expected trend, indicating challenges in generating images that closely resemble real ones. Conclusions: This study highlights both the potential and limitations of GAN in generating realistic bioimages. Future work will focus on improving generation quality and detection accuracy across different datasets. Full article

This study proposes a novel image reconstruction algorithm for nuclear medicine imaging based on the maximum likelihood expectation maximization (MLEM) framework with dynamic ElasticNet regularization. Whereas conventional the L1 and L2 regularization methods involve trade-offs between noise suppression and structural preservation, ElasticNet combines their strengths. Our method further introduces a dynamic weighting scheme that adaptively adjusts the balance between the L1 and L2 terms over iterations while ensuring nonnegativity when using a sufficiently small regularization parameter. We evaluated the proposed algorithm using numerical phantoms (Shepp–Logan and digitized Hoffman) under various noise conditions. Quantitative results based on the peak signal-to-noise ratio and multi-scale structural similarity index measure demonstrated that the proposed dynamic ElasticNet regularized MLEM consistently outperformed not only standard MLEM and L1/L2 regularized MLEM but also the fixed-weight ElasticNet variant. Clinical single-photon emission computed tomography brain image experiments further confirmed improved noise suppression and clearer depiction of fine structures. These findings suggest that our proposed method offers a robust and accurate solution for tomographic image reconstruction in nuclear medicine imaging. Full article

Due to the distinctive attributes of underwater environments, underwater images frequently encounter challenges such as low contrast, color distortion, and noise. Current underwater image enhancement techniques often suffer from limited generalization, preventing them from effectively adapting to a variety of underwater images taken in different underwater environments. To address these issues, we introduce a diffusion model-based underwater image enhancement method using an adversarial learning strategy, referred to as adversarial learning diffusion underwater image enhancement (ALDiff-UIE). The generator systematically eliminates noise through a diffusion model, progressively aligning the distribution of the degraded underwater image with that of a clear underwater image, while the discriminator helps the generator produce clear, high-quality underwater images by identifying discrepancies and pushing the generator to refine its outputs. Moreover, we propose a multi-scale dynamic-windowed attention mechanism to effectively fuse global and local features, optimizing the process of capturing and integrating information. Qualitative and quantitative experiments on four benchmark datasets—UIEB, U45, SUIM, and LSUI—demonstrate that ALDiff-UIE increases the average PCQI by approximately 12.8% and UIQM by about 15.6%. The results indicate that our method outperforms several mainstream approaches in terms of both visual quality and quantitative metrics, showcasing its effectiveness in enhancing underwater images. Full article

This study proposes a method to detect double compression in H.265/HEVC videos containing B-frames, a scenario underexplored in previous research. The method extracts frame-level encoding features—including frame type, coding unit (CU) size, quantization parameter (QP), and prediction modes—and represents each video as a 28-dimensional feature vector. A bidirectional Long Short-Term Memory (Bi-LSTM) classifier is then trained to model temporal inconsistencies introduced during recompression. To evaluate the method, we created a dataset of 129 HEVC-encoded YUV videos derived from 43 original sequences, covering various bitrate combinations and GOP structures. The proposed method achieved a detection accuracy of 80.06%, outperforming two existing baselines. These results demonstrate the practical applicability of the proposed approach in realistic double compression scenarios. Full article

It is well-known that accurate classification of histopathological images is essential for effective diagnosis of colorectal cancer. Our study presents three attention-based decision fusion models that combine pre-trained CNNs (Inception V3, Xception, and MobileNet) with a spatial attention mechanism to enhance feature extraction and focus on critical image regions. A key innovation is the attention-driven fusion strategy at the decision level, where model predictions are weighted by relevance and confidence to improve classification performance. The proposed models were tested on diverse datasets, including 17,531 colorectal cancer histopathological images collected from the Royal Hospital in the Sultanate of Oman and a publicly accessible repository, to assess their generalizability. The performance results achieved high accuracy (98–100%), strong MCC and Kappa scores, and low misclassification rates, highlighting the robustness of the proposed models. These models outperformed individual transfer learning approaches (p = 0.009), with performance differences attributed to the characteristics of the datasets. Gradient-weighted class activation highlighted key predictive regions, enhancing interpretability. Our findings suggest that the proposed models demonstrate the potential for accurately classifying CRC images, highlighting their value for research and future exploration in diagnostic support. Full article

The accurate segmentation of blood vessels and centerline extraction are critical in vascular imaging applications, ranging from preoperative planning to hemodynamic modeling. This study introduces a novel one-stage method for simultaneous vessel segmentation and centerline extraction using a multitask neural network. We designed a hybrid architecture that integrates convolutional and graph layers, along with a task-specific loss function, to effectively capture the topological relationships between segmentation and centerline extraction, leveraging their complementary features. The proposed end-to-end framework directly predicts the centerline as a polyline with real-valued coordinates, thereby eliminating the need for post-processing steps commonly required by previous methods that infer centerlines either implicitly or without ensuring point connectivity. We evaluated our approach on a combined dataset of 142 computed tomography angiography images of the thoracic and abdominal regions from LIDC-IDRI and AMOS datasets. The results demonstrate that our method achieves superior centerline extraction performance (Surface Dice with threshold of 3 mm: 97.65%$\pm$2.07%) compared to state-of-the-art techniques, and attains the highest subvoxel resolution (Surface Dice with threshold of 1 mm: 72.52%$\pm$8.96%). In addition, we conducted a robustness analysis to evaluate the model stability under small rigid and deformable transformations of the input data, and benchmarked its robustness against the widely used VMTK toolkit. Full article

Hand hygiene is paramount for public health, especially in critical sectors like healthcare and the food industry. Ensuring compliance with recommended hand washing gestures is vital, necessitating autonomous evaluation systems leveraging machine learning techniques. However, the scarcity of comprehensive datasets poses a significant challenge. This study addresses this issue by presenting an open synthetic hand washing dataset, created using 3D computer-generated imagery, comprising 96,000 frames (equivalent to 64 min of footage), encompassing eight gestures performed by four characters in four diverse environments. This synthetic dataset includes RGB images, depth/isolated depth images and hand mask images. Using this dataset, four neural network models, Inception-V3, Yolo-8n, Yolo-8n segmentation and PointNet, were trained for gesture classification. The models were subsequently evaluated on a large real-world hand washing dataset, demonstrating successful classification accuracies of 56.9% for Inception-V3, 76.3% for Yolo-8n and 79.3% for Yolo-8n segmentation. These findings underscore the effectiveness of synthetic data in training machine learning models for hand washing gesture recognition. Full article

The early detection of brain tumors is critical for improving clinical outcomes and patient survival. However, medical imaging datasets frequently exhibit class imbalance, posing significant challenges for traditional classification algorithms that rely on balanced data distributions. To address this issue, this study employs a One-Class Support Vector Machine (OCSVM) trained exclusively on features extracted from healthy brain MRI images, using both deep learning architectures—such as DenseNet121, VGG16, MobileNetV2, InceptionV3, and ResNet50—and classical feature extraction techniques. Experimental results demonstrate that combining Convolutional Neural Network (CNN)-based feature extraction with OCSVM significantly improves anomaly detection performance compared with simpler handcrafted approaches. DenseNet121 achieved an accuracy of 94.83%, a precision of 99.23%, and a sensitivity of 89.97%, while VGG16 reached an accuracy of 95.33%, a precision of 98.87%, and a sensitivity of 91.32%. MobileNetV2 showed a competitive trade-off between accuracy (92.83%) and computational efficiency, making it suitable for resource-constrained environments. Additionally, the pure CNN model—trained directly for classification without OCSVM—outperformed hybrid methods with an accuracy of 97.83%, highlighting the effectiveness of deep convolutional networks in directly learning discriminative features from MRI data. This approach enables reliable detection of brain tumor anomalies without requiring labeled pathological data, offering a promising solution for clinical contexts where abnormal samples are scarce. Future research will focus on reducing inference time, expanding and diversifying training datasets, and incorporating explainability tools to support clinical integration and trust in AI-based diagnostics. Full article

Multimodal sensing is essential in order to reach the robustness required of autonomous vehicle perception systems. Infrared (IR) imaging is of particular interest due to its low cost and complementarity with traditional RGB sensors. However, the lack of IR data in many datasets and simulation tools limits the development and validation of sensor fusion algorithms that exploit this complementarity. To address this, we propose an augmentation method that synthesizes realistic IR data from RGB images using gradient-boosting decision trees. We demonstrate that this method is an effective alternative to traditional deep learning methods for image translation such as CNNs and GANs, particularly in data-scarce situations. The proposed approach generates high-quality synthetic IR, i.e., Near-Infrared (NIR) and thermal images from RGB images, enhancing datasets such as MS2, EPFL, and Freiburg. Our synthetic images exhibit good visual quality when evaluated using metrics such as $R^2$, PSNR, SSIM, and LPIPS, achieving an $R^2$ of 0.98 on the MS2 dataset and a PSNR of $21.3$ dB on the Freiburg dataset. We also discuss the application of this method to synthetic RGB images generated by the CARLA simulator for autonomous driving. Our approach provides richer datasets with a particular focus on IR modalities for sensor fusion along with a framework for generating a wider variety of driving scenarios within urban driving datasets, which can help to enhance the robustness of sensor fusion algorithms. Full article

The prediction of anthropometric measurements from 2D body images, particularly for children, remains an under-explored area despite its potential applications in healthcare, fashion, and fitness. While pose estimation and body shape classification have garnered extensive attention, estimating body measurements and body mass index (BMI) from images presents unique challenges and opportunities. This paper provides a comprehensive review of the current methodologies, focusing on deep-learning approaches, both standalone and in combination with traditional machine-learning techniques, for inferring body measurements from facial and full-body images. We discuss the strengths and limitations of commonly used datasets, proposing the need for more inclusive and diverse collections to improve model performance. Our findings indicate that deep-learning models, especially when combined with traditional machine-learning techniques, offer the most accurate predictions. We further highlight the promise of vision transformers in advancing the field while stressing the importance of addressing model explainability. Finally, we evaluate the current state of the field, comparing recent results and focusing on the deviations from ground truth, ultimately providing recommendations for future research directions. Full article