Background: As an important part of the tongue, the tongue coating is closely associated with different disorders and has major diagnostic benefits. This study aims to construct a neural network model that can perform complex tongue coating segmentat...
Optical coherence tomography (OCT) is used to obtain retinal images and stratify them to obtain the thickness of each intraretinal layer, which plays an important role in the clinical diagnosis of many ophthalmic diseases. In order to overcome the di...
This study presents a segmentation framework for images of 220 kV cable insulation that addresses sample scarcity and blurred boundaries. The framework integrates data augmentation using the Wasserstein Generative Adversarial Network with Gradient Pe...
The East China Sea (ECS) is a globally active region for internal solitary waves (ISWs); however, its overall spatiotemporal distribution remains poorly understood. To address this gap, this study proposes a deep learning method based on multi-source...
Medical image segmentation presents substantial challenges arising from the diverse scales and morphological complexities of target anatomical structures. Although existing Transformer-based models excel at capturing global dependencies, they encount...
The structure and geomorphology of channel systems play a critical role in interpreting sedimentary processes and characterizing subsurface reservoir capacity. This study presents an innovative 3D DS-TransUnet model for seismic channel interpretation...
Accurate delineation of individual agricultural plots, the foundational units for agriculture-based activities, is crucial for effective government oversight of agricultural productivity and land utilization. To improve the accuracy of plot segmentat...
Internal Solitary Waves (ISWs) play a crucial role in energy transfer among multi-scale oceanic motions. They also have a significant impact on marine transportation and underwater communication. To date, the identification of ISWs has been primarily...
Concrete surface crack detection is a critical problem in the health monitoring and maintenance of engineering structures. The existence and development of cracks may lead to the deterioration of structural performance, potentially causing serious sa...
Transformer models have achieved great results in the field of computer vision over the past 2 years, drawing attention from within the field of remote sensing. However, there are still relatively few studies on this model in the field of remote sens...
The Yellow River Delta (YRD) is a vital agricultural and ecologically fragile zone in China. Understanding the spatial pattern and evolutionary characteristics of Rural Settlements Area (RSA) in this region is crucial for both ecological protection a...
Automatic retinal vessel segmentation is important for assisting clinicians in diagnosing ophthalmic diseases. The existing deep learning methods remain constrained in instance connectivity and thin vessel detection. To this end, we propose a novel a...
In this paper, we address the problem of forest fires’ early detection and segmentation in order to predict their spread and help with fire fighting. Techniques based on Convolutional Networks are the most used and have proven to be efficient at solv...
The automatic reading recognition of pointer meters plays a crucial role in data monitoring and analysis in intelligent substations. Existing meter reading methods struggle to address challenging difficulties such as image distortion and varying illu...
Accurate lung segmentation plays a vital role in generating 3D projections of lung infections, which contribute to the diagnosis and treatment planning of various lung diseases, including cases like COVID-19. This study capitalizes on the capabilitie...
Oil spills pose a significant threat to the marine ecological environment. The intelligent interpretation of synthetic aperture radar (SAR) remote sensing images serves as a crucial approach to marine oil spill detection, offering the potential for r...
The solidified state after the melting of the forming layer in the laser powder bed fusion (LPBF) directly reflects the final forming quality. Compared with the powder layer and the melt pool, it is easier to recognize and remove the defects of conto...
The transformer-based U-Net network structure has gained popularity in the field of medical image segmentation. However, most networks overlook the impact of the distance between each patch on the encoding process. This paper proposes a novel GC-Tran...
Cardiovascular disease (CVD) is a disease with high mortality in modern times. The segmentation task for MRI to extract the related organs for CVD is essential for diagnosis. Currently, a large number of deep learning methods are designed for medical...
To enhance the segmentation accuracy of car front face elements such as headlights and grilles for car front face design, and to improve the superiority and efficiency of solutions in automotive partial modification design, this paper introduces MD-T...
Manual inspection methods traditionally used for tunnel lining leakage suffer from high subjectivity and low efficiency. At the same time, existing detection models do not perform well in terms of accuracy when faced with complex scenarios; this make...
Lung ultrasound (LUS) is a non-invasive bedside imaging technique for diagnosing pulmonary conditions, especially in critical care settings. A-lines and B-lines are important features in LUS images that help to assess lung health and identify changes...
Accurate segmentation is essential for creating digital twins based on volumetric images for high fidelity composite material analysis. Conventional techniques typically require labor-intensive and time-consuming manual effort, restricting their prac...
Background: Colon capsule endoscopy (CCE) has seen increased adoption since the COVID-19 pandemic, offering a non-invasive alternative for lower gastrointestinal investigations. However, inadequate bowel preparation remains a key limitation, often le...
In the application of change detection satellite remote sensing images, synthetic aperture radar (SAR) images have become a more important data source. This paper proposes a new end-to-end SAR image change network architecture—TransUNet++SAR&md...
The change detection of urban buildings is currently a hotspot in the research area of remote sensing, which plays a vital role in urban planning, disaster assessments and surface dynamic monitoring. SAR images have unique characteristics compared wi...
Traditional manual inspection methods for tunnel lining leakage are subjective and inefficient, while existing models lack sufficient recognition accuracy in complex scenarios. An intelligent leakage identification model adaptable to complex backgrou...
Sea fog is a weather hazard along the coast and over the ocean that seriously threatens maritime activities. In the deep learning approach, it is difficult for convolutional neural networks (CNNs) to fully consider global context information in sea f...
Background/Objectives: The automatic left ventricle segmentation in transesophageal echocardiography (TEE) is of significant importance. In this paper, we constructed a large-scale TEE apical four-chamber view (A4CV) image dataset and proposed an aut...
Abrasive belt wear has an important impact on the dimensional accuracy and surface quality of parts. Accurate quantitative measurement of abrasive belt wear is an important basis for optimizing grinding process parameters, but also a very challenging...
Wildfires are a worldwide natural disaster causing important economic damages and loss of lives. Experts predict that wildfires will increase in the coming years mainly due to climate change. Early detection and prediction of fire spread can help red...
Background: Liver cancer has a high mortality rate worldwide, and clinicians segment liver vessels in CT images before surgical procedures. However, liver vessels have a complex structure, and the segmentation process is conducted manually, so it is...
At present, some aging populations, such as those in Japan, face an underlying risk of inadequate medical resources. Using neural networks to assist doctors in locating the aorta in patients via computed tomography (CT) before surgery is a task with...
To support dual-carbon objectives and enhance the accuracy of rooftop distributed photovoltaic (PV) planning, this study proposes a multidimensional coupled evaluation framework that integrates an improved rooftop segmentation network (CESW-TransUNet...
Substation equipment operating in harsh environments is highly susceptible to corrosion, yet conventional image segmentation methods often fail to achieve precise delineation of corroded regions. Here, we propose an enhanced TransU-Net-based approach...
Deep learning-based semantic segmentation algorithms have proven effective in landslide detection. For the past decade, convolutional neural networks (CNNs) have been the prevailing approach for semantic segmentation. Nevertheless, the intrinsic limi...
Farmland shelterbelts are crucial elements in safeguarding agricultural ecological security and sustainable development, with their precise extraction being vital for regional ecological monitoring and precision agriculture management. However, const...
Optical coherence tomography (OCT) provides unique advantages in ophthalmic examinations owing to its noncontact, high-resolution, and noninvasive features, which have evolved into one of the most crucial modalities for identifying and evaluating ret...
Background/Objectives: Radial artery tracking (RAT) in the short-axis view is a pivotal step for ultrasound-guided radial artery catheterization (RAC), which is widely employed in various clinical settings. To eliminate disparities and lay the founda...
Digital epidermal microscopic (DEM) images offer the potential to quantitatively analyze skin aging at the microscopic level. However, stochastic complexity, local highlights, and low contrast in DEM images pose significant challenges to accurate seg...
Traditional U-shape segmentation models can achieve excellent performance with an elegant structure. However, the single-layer decoder structure of U-Net or SwinUnet is too “thin” to exploit enough information, resulting in large semantic...
Manual labeling of lesions in medical image analysis presents a significant challenge due to its labor-intensive and inefficient nature, which ultimately strains essential medical resources and impedes the advancement of computer-aided diagnosis. Thi...
Accurate segmentation of water bodies from high-resolution remote sensing imagery is crucial for water resource management and ecological monitoring. However, small and morphologically complex water bodies remain difficult to detect due to scale vari...
This study proposes a Swin-ReshoUnet architecture with a three-level enhancement mechanism to address inefficiencies in multi-scale feature extraction and gradient degradation in deep networks for high-precision seismic exploration. The encoder uses...
Medical image segmentation is crucial for medical image processing and the development of computer-aided diagnostics. In recent years, deep Convolutional Neural Networks (CNNs) have been widely adopted for medical image segmentation and have achieved...
As the lakes located in the Qinghai-Tibet Plateau are important carriers of water resources in Asia, dynamic changes to these lakes intuitively reflect the climate and water resource variations of the Qinghai-Tibet Plateau. To address the insufficien...
The production of long-term landslide maps (LAM) holds crucial importance in estimating landslide activity, vegetation disturbance, and regional stability. However, the availability of LAMs remains limited in many regions, despite the application of...
Ultrasound is one of the most commonly used imaging methodologies in obstetrics to monitor the growth of a fetus during the gestation period. Specifically, ultrasound images are routinely utilized to gather fetal information, including body measureme...
Background/Objectives: Accurate segmentation of brain stroke lesions from MRI images is a critical task in medical image analysis that is essential for timely diagnosis and treatment planning. Methods: This paper presents a novel approach for segment...
In high-resolution remote sensing images, there are areas with weak textures such as large building roofs, which occupy a large number of pixels in the image. These areas pose a challenge for traditional semantic segmentation networks to obtain ideal...
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