Search Results (5,033)

Water level monitoring is closely linked to the safety of production and daily activities along riverbanks, making real-time and high-precision water level measurement an urgent technical demand. The feature extraction backbone of the Unet model is modified, and the lightweight MobileNet V2 network is adopted in this paper. The constructed network achieves significantly higher computational efficiency than standard convolutions, effectively overcoming the limited real-time performance of conventional water level measurement methods. Furthermore, the coordinate attention (CA) mechanism is integrated into the skip connections of Unet to strengthen the network’s capability to extract key features for water level segmentation, thereby further improving the accuracy of water level detection. A novel piecewise linear fitting method for water level line measurement based on monocular vision is proposed, and field-measured water level data are adopted to verify the calculation results. The main achievements of the improved model include the following: (1) Compared with the baseline model, the improved model MCUnet (MobileNet V2 + CA + Unet) achieves a 5.77% increase in accuracy and a 25.71% improvement in inference speed on the experimental water surface recognition dataset. (2) Taking the field-observed water level as the reference, the mean absolute error of the proposed image-based water level monitoring method reaches approximately 1.69 cm. (3) In comparison with DeepLab, U2net and Unet, the MCUnet model gains accuracy improvements of 4.47%, 2.81% and 5.77% respectively, with the detection frame rate increased by 12 FPS, 15 FPS and 11 FPS correspondingly. Through this work, the paper can provide some theoretical support and technical references for overcoming the limitations of conventional water level measuring devices, including strict installation requirements, limited measurement precision, high deployment and maintenance costs, and cumbersome data processing. Full article

Beat-level synchrony among cardiomyocyte clusters is a critical indicator of cardiac electromechanical function. Traditional invasive approaches have substantial limitations, and conventional computer vision methods are poorly suited for resolving densely packed, adherent clusters. To address these challenges, we developed an analysis framework to characterize the beating characteristics of cardiomyocyte clusters from microscopic imaging data. Specifically, we propose CardioSegNet, a multi-task deep learning model that combines attention mechanisms with three prediction heads (semantic segmentation, contour detection, and distance transform), followed by a watershed algorithm to achieve high-accuracy cluster-level segmentation of cardiomyocyte clusters. The Pixel-Difference method is applied to extract time-series beating signals from each segmented cluster and compute several dynamic parameters, including beating amplitude, period, frequency, and the Beat Rate Irregularity (BRI). We further introduce PeriodAwareNAPTD_ij to quantify the beating synchrony among different clusters. Our experimental results show that CardioSegNet achieves a Dice coefficient of 0.8868 and an HD95 of 93.02 µm on an independent test set, demonstrating strong segmentation performance. The cardiomyocyte populations are not uniformly globally synchronized; rather, they consist of multiple local subgroups with high internal synchrony, and the degree of synchronization between clusters is positively correlated with their physical distance. This label-free analytical pipeline provides an efficient tool for myocardial function evaluation and cardiotoxicity screening in vitro. Full article

Three-dimensional (3D) reconstruction from a photogrammetric perspective aims to infer the geometric structure of a scene from a set of images, including the recovery of depth information inherently lost during image acquisition. Conventional photogrammetric pipelines rely on multiple handcrafted processing stages, often requiring manual intervention. This work introduces a dataset designed to support the study of background removal techniques in photogrammetric workflows through salient object detection (SOD). The dataset comprises 15,120 images divided into sets of 28 distinct objects, each set including 36 high-resolution RGB images captured from multiple viewpoints. Additionally, each set provides 36 manually segmented images, as well as automatically segmented versions obtained using four different SOD algorithms. To facilitate evaluation and reproducibility, 153 reconstructed 3D models are provided across all object categories, and a 3D reconstruction evaluation methodology based on the Chamfer Distance metric is proposed, enabling the analysis of the impact of different segmentation strategies on 3D reconstruction. The dataset offers a benchmark resource for the development, comparison, and validation of methods aimed at improving photogrammetric pipelines through automated information filtering. Full article

Glaucoma is a leading cause of irreversible vision loss, and its early diagnosis remains critically important yet challenging. Traditional assessment based on the cup-to-disc ratio is often insufficient at early stages, whereas the retinal vascular network can provide additional quantitative biomarkers. This study develops and validates a binary classification method for distinguishing healthy from glaucomatous fundus images by combining deep-learning-based vessel segmentation, fractal and multifractal analysis, and textural features. The public ORIGA dataset is utilized. Images are converted to grayscale using three alternative approaches, followed by Gray-Level Co-occurrence Matrix texture analysis and fractal analysis based on the differential box-counting method. Vessel segmentation is implemented via a U-Net neural network trained on a combination of public datasets, after which multifractal analysis is performed on the resulting binary masks. The extracted features are used to train and compare several machine learning models with hyperparameter optimization. The best-performing model among ONH-based features (Random Forest) achieves 75.00%; however, a logistic regression model using multifractal parameters and CDR reaches 86.17%, substantially outperforming the CDR-only baseline (66.15%). Notably, while classical fractal dimension shows only marginal differences (1–2% relative change) between groups, multifractal parameters reveal distinct changes: the multifractal spectrum width $\Delta \alpha$ increases markedly and the minimum singularity exponent ${\alpha }_{\min }$ decreases in glaucomatous eyes, indicating increased heterogeneity of the vascular network. These findings suggest that multifractal characteristics of the vascular network can serve as reliable and sensitive biomarkers for automated glaucoma screening, offering clear advantages over classical fractal analysis. Full article

Macular holes are uncommon in pediatric patients and are most often associated with ocular trauma. Idiopathic cases are rare and may present as subtle clinical findings and atypical imaging features. We report a case of a 13-year-old boy presenting with decreased visual acuity in the left eye. Best-corrected visual acuity was 1.0 in the right eye and 0.5 in the left eye, with unremarkable anterior segment examination. Optical coherence tomography showed a foveal defect characterized by a central hyporeflective cavity with disruption of retinal layers, without evidence of a full-thickness defect. Fluorescein angiography demonstrated central hyperfluorescence without leakage. Color fundus photography revealed a subtle central foveal lesion, while electrophysiological testing and visual field examination were within normal limits. This case highlights that early structural abnormalities of the fovea in pediatric patients may present with minimal clinical findings and preserved retinal function. Multimodal imaging, particularly OCT, plays a key role in detecting subtle foveal alterations and may aid in identifying early stages within the spectrum of macular hole formation. Careful monitoring is warranted due to the potential for progression. Full article

Rapid and non-destructive detection of nitrogen content in greenhouse cucumber leaves is essential for precision fertilization, yet traditional chemical methods are destructive and time-consuming, and existing spectral technologies suffer from high cost and poor field adaptability. This study aims to propose a high-precision detection scheme for cucumber leaf nitrogen content based on a lightweight model, suitable for complex scenarios. A total of 698 cucumber leaf images covering three growth stages were collected to build a segmentation dataset. Four categories and eight types of deep learning segmentation models were optimized and compared, and the optimal one was selected to extract leaf regions. Nine color features were extracted and combined with Kjeldahl-measured nitrogen content to construct and optimize three machine learning models, forming a deep learning segmentation–color feature extraction–machine learning prediction process. The results showed that YOLOv11n achieved the best segmentation accuracy, with an IoU of 0.9212 and AP of 0.9998 for high-resolution images. The optimized XGBoost had the highest prediction accuracy, with an MAE of 0.469, MSE of 0.461, and RMSE of 0.679, which are 10.15%, 8.71%, and 4.36% lower than Support Vector Regression with Radial Basis Function kernel (SVR_RBF) respectively, and its predicted nitrogen content aligned well with true values. The proposed scheme integrating YOLOv11n and XGBoost offers a lightweight technical solution for nitrogen nutrition diagnosis and precise fertilization of greenhouse cucumbers. Full article

Image-based measurement of chopped maize stalk length remains difficult because the fragments are often slender, curved, touching, or partly overlapped. Bounding-box dimensions are therefore not reliable for length estimation, and manual measurement is too slow for repeated quality assessment. In this study, we developed a YOLO11n-based visual framework for measuring chopped maize stalk length under fixed imaging conditions. The dataset contained 1127 images collected on a laboratory platform and covered stalk lengths of 10–150 mm, different moisture states, and isolated, touching, and overlapping arrangements. To obtain more stable regions of interest, the YOLO11n detector was modified with large separable kernel attention (LSKA), a lightweight cross-scale decoupled detection (LSCD) head, and Wise intersection over union version 3 (WIoU v3). The detected stalk regions were then processed by local segmentation, morphological refinement, skeleton extraction, longest-path calculation, and washer-based scale conversion. The modified detector reached 94.8% precision, 90.4% recall, 96.5% mAP@0.5, and 71.1% mAP@0.5:0.95, with a detector inference speed of 174 FPS. In the length-measurement test, the mean relative errors were 5.8%, 8.3%, and 10.4% for the <40 mm, 40–80 mm, and >80 mm groups, respectively. Across all evaluated fragments, the complete pipeline produced an MAE of 6.0 mm, an RMSE of 9.4 mm, and a mean relative error of 8.2%. The framework therefore provides a practical way to measure chopped maize stalk length under controlled imaging conditions, although long, curved, and cluttered fragments still caused most of the remaining errors. Full article

The digitization of existing building stock often depends on legacy 2D raster floor plans (scanned drawings, PDF exports, or photographs) because structured building information models are frequently unavailable for older properties. Manual measurement and visual inspection of such documents are time consuming and error prone. This paper presents an integrated deep learning pipeline that extracts semantic information from unstructured two-dimensional floor plan images of existing structures and supports preliminary compliance screening via locally deployed large language models. The pipeline employs YOLOv8 for the localization and classification of 18 architectural symbols and furniture items, and a U-Net with a ResNet34 encoder for the semantic segmentation of walls and interior room spaces. To translate pixel-level predictions into physical metrics, we implement an area calculation module based on user-defined reference scale calibration. An LLM evaluation module, deployed locally via Ollama with a retrieval-augmented generation pipeline, interprets extracted room metrics and flags potential non-compliance against referenced residential design guidelines; it is intended for the assessment of existing layouts rather than generative co-design. We expand a core dataset of 101 manually annotated source floor plans to 303 augmented instances using label-aligned geometric transformations, while reporting generalization in terms of the 101 unique source plans. On the held-out validation split (10 source plans), YOLOv8 achieves 92.3% mAP50 versus 87.2% for a Faster R-CNN reference model on the same data split (detection baselines differ in training epochs and pretraining; see Experiments); U-Net achieves 95.71% mIoU, surpassing DeepLabv3+ (93.2%) under matched segmentation training settings. The system is deployed as an interactive web application for legacy building survey and preliminary regulatory review when only two-dimensional documentation is available. Full article

Tree-derived bio-based polymer materials, including natural rubber, raw lacquer, pine resin, and tree gums, are important renewable resources for sustainable forestry and green manufacturing. However, their collection still largely depends on manual operations, which may cause unstable yield, tree damage, and low operational efficiency. This review examines intelligent identification technologies for tree-derived material collection from the perspectives of multimodal perception and machine learning. The collection requirements and recognition targets of typical materials are first analyzed, including trunk localization, tapping line detection, bark feature extraction, tree state assessment, and safe tool–bark interaction. Visual, RGB-D, LiDAR, spectral, force/tactile, and environmental sensing technologies are then reviewed, and their roles in complex forest perception and robotic operation are discussed. Machine learning methods, including traditional classifiers, object detection, image segmentation, point cloud processing, temporal modeling, few-shot learning, transfer learning, and uncertainty-aware evaluation, are further examined. Representative cases in rubber tapping, lacquer collection, and pine resin harvesting are compared to reveal the transition from single-sensor recognition to perception–decision–execution integration. Key challenges are identified in dataset standardization, model generalization, edge deployment, force-aware control, and biological mechanism integration. Future directions are proposed toward autonomous, low-damage, and high-yield intelligent collection systems. Full article

Background/Objectives: Spontaneous intracranial hypotension (SIH) is caused by spinal cerebrospinal fluid (CSF) leakage and is typically diagnosed by clinical presentation and characteristic MRI signs; however, objective tools for monitoring physiological changes and treatment response remain limited. Cine phase-contrast MRI (PC-MRI) enables noninvasive quantification of aqueductal CSF dynamics, yet reliable analysis is challenging since the cerebral aqueduct is extremely small and susceptible to low contrast, partial volume effects, and ROI-dependent measurement variability—particularly in SIH where CSF pulsatility is often reduced. Methods: We propose an end-to-end automated framework that integrates (1) a cascade localization–segmentation strategy, consisting of Tiny YOLOv4 detection followed by MultiResUNet segmentation on a YOLOv4-derived cropped ROI; (2) physiology-informed pulsatility-based segmentation (PUBS) to refine anatomical masks into functional flow ROIs; and (3) one-dimensional convolutional neural networks (1D-CNNs) to extract exploratory waveform morphology features from 32-phase cardiac-cycle velocity waveforms. The study includes 39 participants, yielding 59 cine PC-MRI examinations: 11 controls, 28 Pre-treatment SIH scans and 20 Post-treatment Recovery scans. Results: The cascade model significantly improves segmentation robustness compared with a full-image baseline, achieving higher Dice scores and markedly lower boundary errors across cohorts (e.g., Pre-treatment SIH HD95: 1.66 ± 0.74 px vs. 15.37 ± 44.98 px). PUBS refinement reduces quantification deviation from expert manual references in SIH (mean relative error: 7.4% to 5.6%) and improves diagnostic performance for multiple hemodynamic parameters (e.g., downward mean flow AUC: 0.747 to 0.792). For waveform morphology analysis, the end-to-end 1D-CNN classifier was evaluated using repeated-seed participant-level grouped LOOCV. The repeated-seed ensemble prediction showed modest out-of-sample discrimination between Normal controls and Pre-treatment SIH scans, with an AUC of 0.646, a bootstrap 95% confidence interval of 0.455–0.826, and a permutation-test p-value of 0.072. Separately, exploratory analysis of the final baseline-trained 1D-CNN latent space showed marked, apparent Normal-versus-SIH separability and an intermediate recovery distribution in PCA space, suggesting that aqueductal waveform morphology may encode SIH-related physiological information. Conclusions: These findings suggest that SIH-related information may be reflected not only in flow magnitude but also in aqueductal CSF waveform morphology. However, the modest and statistically non-significant out-of-sample performance of the end-to-end 1D-CNN classifier indicates that morphology-based AI features should currently be regarded as exploratory biomarker candidates rather than validated stand-alone diagnostic tools. Larger independent cohorts are required to confirm their reproducibility, physiological meaning, and clinical utility. Full article

Immunohistochemistry (IHC) is a widely used method for detecting specific proteins in tissue samples, helping diagnose diseases such as cancer. Traditional analysis methods rely heavily on human interpretation, which can lead to inconsistencies. In this study, we propose DeepFlare, a weakly supervised deep learning framework for cross-modality translation and segmentation of immunofluorescence and immunohistochemistry images. The proposed method utilizes multiplex immunofluorescence (mpIF) and co-registered IHC images, combined with preprocessing techniques such as affine transformation, stain normalization, noise reduction, and artifact removal. Multiple imaging channels, including hematoxylin, DAPI, Lap2, and nuclear envelope signals, are leveraged to generate segmentation masks using a U-Net++ architecture. The final segmentation mask is obtained through weighted fusion of modality-specific outputs. A generative adversarial network (GAN) is employed to measure translation fidelity between generated and real images. Weakly supervised learning techniques, including image-level supervision and consistency constraints, are applied to enhance performance under limited annotation scenarios. Pretrained pathology foundation encoders such as UNI and Virchow are integrated to extract multi-scale morphological and contextual features. Explainable AI techniques are incorporated to highlight critical regions and refine model attention. Experimental results demonstrate strong performance, achieving an SSIM of 0.7077 for image translation and a Dice score of 0.7424 for segmentation. The integration of the UNI encoder provides marginal improvement over the baseline (0.72 Dice score), indicating limited domain adaptation without fine-tuning on the dataset of 1264 training samples. Full article

Accurate and automated extraction of solid waste sites from remote-sensing imagery constitutes a pivotal demand for contemporary environmental regulation and risk mitigation. However, in high-resolution remote-sensing imagery, solid waste sites are typically represented as a single semantic image object (SIO), which is composed of multiple physical image parcels (PIPs) exhibiting significant variations in scale, morphology, and spectral properties. This intrinsic heterogeneity substantially increases the complexity and uncertainty of multi-class site identification. To address this challenge, this paper proposes WasteMOE Net (WMN), which is developed based on the core concept of modeling the SIO–PIP relationship. WMN adopts a heterogeneous expert selection mechanism combined with a nested mixture-of-experts architecture. It thus enables adaptive perception of complex PIPs across diverse scenarios and their integrated discrimination at the SIO level. In addition, by incorporating the explicit nonlinear representation capability of the KAN network, WMN effectively improves multi-class recognition accuracy while maintaining computational efficiency. Furthermore, this study constructs a high-resolution solid waste site dataset in accordance with the SIO–PIP-aware annotation principle, encompassing five representative categories: tailings ponds (TP), construction spoil sites (CSS), landfill sites (LS), garbage dump sites (GDS), and excavation sites (ES). Experimental results show that WMN achieves mAP50 values of 74.2% (GDS), 63.5% (CSS), 80.9% (ES), 85.4% (TP), and 83.1% (LS) in detection tasks, and 75.4%, 64.1%, 83.0%, 86.7%, and 84.1% for the corresponding categories in segmentation tasks. It achieves competitive performance compared with state-of-the-art methods in both tasks. Further, in a real-world application over Loudi City, China, WMN completed the processing of a 490.67 km² area within 1.34 h. The recognition accuracies for GDS and ES reached 54.8% and 65.3%, respectively. Finally, the proposed method has been successfully integrated into a GIS-based solid waste pollution risk prevention system, which markedly boosts the overall efficiency of environmental monitoring and on-site inspections. Full article

Lung cancer remains the leading cause of cancer-related mortality worldwide, largely due to late-stage diagnosis, biological heterogeneity, and persistent challenges in staging and treatment selection. This narrative review summarizes current and emerging applications of AI across lung cancer screening and early detection, imaging-based staging and prognostication, tissue and liquid biopsy-based tumor characterization, treatment planning, surgical and intraoperative guidance, and drug discovery. In imaging, deep learning models have demonstrated high performance in pulmonary nodule detection, risk stratification, and prediction of molecular alterations, while also showing promise in improving screening efficiency and reducing interpretive variability. In pathology and liquid biopsy domains, AI enables prediction of driver mutations, immunotherapy response, and survival outcomes directly from histopathology slides, circulating tumor DNA, and other blood-based biomarkers, facilitating minimally invasive precision oncology approaches. In treatment planning and delivery, AI systems are being developed to support clinical decision-making, surgical planning (through advanced image segmentation and delineation of operative anatomy), and intraoperative navigation through robotic and computer vision-enabled platforms. Despite these advances, significant barriers remain, including limited real-world validation, algorithmic biases, workflow integration issues, and unresolved ethical and legal concerns. Future progress will depend on the development of transparent, clinically validated, and generalizable AI systems that augment rather than replace the expertise of clinical providers and healthcare teams. Active engagement from pulmonologists, oncologists, radiologists, and thoracic surgeons will be essential in guiding safe implementation and ensuring that AI-driven innovations translate into meaningful improvements in patient outcomes. Full article

In this paper, a novel approach is presented for fusing RGB and LiDAR inputs for semantic segmentation. Accurate building detection is required for various scenarios such as urban planning or environmental monitoring. The two main sources for accurate building segmentation are either RGB aerial images or LiDAR point clouds covering the selected area. Each of these sources has its own well-known techniques for segmentation; however, for the combination of the input, there are not many architectures available, and extracting different features from the two different fields can result in an enhanced segmentation map. The authors of this article created a semantic segmentation model that uses both the aerial RGB image and the LiDAR point cloud as its input. The network first takes the point cloud and forwards the processed projection to a modified U-Net-based architecture, which fuses the extracted features of the 3D input with the extracted information of the 2D input on each level of the decoding. To train and test the presented model, the authors used a dataset containing more than 3000 images and their corresponding 3D point clouds of three different areas from Hungary. As is also presented in this paper, this approach provides significantly better results than the traditional RGB, Point Cloud segmentation models, and their ensembles in terms of segmentation accuracy. 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