Automatic extraction of cerebral vessels and cranial nerves has important clinical value in the treatment of trigeminal neuralgia (TGN) and hemifacial spasm (HFS). However, because of the great similarity between different cerebral vessels and betwee...
Accurate identification of the photovoltaic development status, material, and form of industrial building roofs is essential for delineating roofs available for development and conducting differentiated photovoltaic potential assessments. This study...
Accurate segmentation of the airway tree is crucial for the diagnosis and intervention of pulmonary disease; however, delineating small peripheral airways remains challenging. The small size and complex branching of distal airways, combined with the...
Agricultural robots and unmanned farmland management require real-time and precise parsing of crop leaves at the edge to support variable application of pesticides, seedling condition monitoring, and phenotypic analysis. However, the field environmen...
Semantic segmentation, as a pixel-level recognition task, has been widely used in a variety of practical scenes. Most of the existing methods try to improve the performance of the network by fusing the information of high and low layers. This kind of...
Colonoscopy is a crucial method for colorectal cancer screening and the early detection of precancerous polyps; however, accurate polyp segmentation is challenged by factors such as indistinct boundaries, minute or flat structures, and regions of hig...
Semantic segmentation is a fundamental task in computer vision that aims to assign a categorical label to each pixel in an image, facilitating dense and detailed scene understanding. This pixel-level classification is especially crucial in autonomous...
High-resolution remote sensing image segmentation is important for urban mapping but remains challenging because of spectral ambiguity, large scale variations, fragmented elongated structures, and background interference. This study aims to improve s...
Cerebrovascular diseases, including stroke and intracranial aneurysm, affect millions worldwide and remain a leading cause of mortality and disability. Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) enables non-invasive visualization of intr...
Automated crack detection is vital for structural maintenance in areas such as construction, roads, and bridges. Accurate crack detection allows for the timely identification and repair of cracks, reducing safety risks and extending the service life...
Road extraction from remote sensing images is of great significance to urban planning, navigation, disaster assessment, and other applications. Although deep neural networks have shown a strong ability in road extraction, it remains a challenging tas...
Accurate extraction of plastic greenhouses from high-resolution remote sensing imagery is essential for agricultural resource management and facility-based crop monitoring. However, the dense spatial distribution, irregular morphology, and complex ba...
Imaging-based spatial transcriptomics has advanced from low-plex single-molecule fluorescence in situ hybridization to a diverse set of highly multiplexed platforms, with recent multimodal and pathology-compatible capabilities. Despite major differen...
With the continuous advancement of deep neural networks, salient object detection (SOD) in natural images has made significant progress. However, SOD in optical remote sensing images (ORSI-SOD) remains a challenging task due to the diversity of objec...
Early detection of esophageal cancer has always been difficult, thereby reducing the overall five-year survival rate of patients. In this study, semantic segmentation was used to predict and label esophageal cancer in its early stages. U-Net was used...
Polar codes have garnered significant attention since they were proposed due to their capacity-achieving properties and their low-complexity decoding process, and they were selected as the coding scheme for the control channel in 5G mobile communicat...
Rapid and accurate mapping of earthquake-triggered landslides from satellite imagery is critical for emergency response and hazard assessment, yet remains challenging due to irregular boundaries, extreme size variations, and atmospheric noise. This p...
Deep learning is increasingly integrated into oral rehabilitation workflows, particularly in implant planning, prosthodontic design automation, and peri-implant diagnosis. However, reported performance is heterogeneous and difficult to compare across...
Land-cover segmentation, a fundamental task within the domain of remote sensing, boasts a broad spectrum of application potential. We address the challenges in land-cover segmentation of remote-sensing imagery and complete the following work. Firstly...
Structural health monitoring and condition assessment of existing bridge decks is a growing challenge. Conventional manned inspections are costly, labor-intensive, and often risky to execute. Sub-surface delamination, a leading cause of deck replacem...
Background/Objectives: Accurate liver and tumor detection and segmentation are crucial in diagnosis of early-stage liver malignancies. As opposed to manual interpretation, which is a difficult and time-consuming process, accurate tumor detection usin...
The precise segmentation of bladder tumors from MRI is essential for bladder cancer diagnosis and personalized therapy selection. Limited by the properties of tumor morphology, achieving precise segmentation from MRI images remains challenging. In re...
The semantic segmentation of outdoor images is the cornerstone of scene understanding and plays a crucial role in the autonomous navigation of robots. Although RGB–D images can provide additional depth information for improving the performance...
Transformers have demonstrated remarkable accomplishments in several natural language processing (NLP) tasks as well as image processing tasks. Herein, we present a deep-learning (DL) model that is capable of improving the semantic segmentation netwo...
Nasopharyngeal Carcinoma segmentation in magnetic resonance imagery (MRI) is vital to radiotherapy. Exact dose delivery hinges on an accurate delineation of the gross tumor volume (GTV). However, the large-scale variation in tumor volume is intractab...
Accurate brain tissue segmentation of MRI is vital to diagnosis aiding, treatment planning, and neurologic condition monitoring. As an excellent convolutional neural network (CNN), U-Net is widely used in MR image segmentation as it usually generates...
Chip defect detection is a crucial aspect of the semiconductor production industry, given its significant impact on chip performance. This paper proposes a lightweight neural network with dual decoding paths for LED chip segmentation, named LDDP-Net....
Medical lesion segmentation remains challenging due to ambiguous boundaries, heterogeneous appearances, and the limited ability of visual features alone to capture clinically meaningful semantics. Existing vision-language segmentation methods have de...
Benefiting from the booming of deep learning, the state-of-the-art models achieved great progress. But they are huge in terms of parameters and floating point operations, which makes it hard to apply them to real-time applications. In this paper, we...
Visual semantic segmentation, which is represented by the semantic segmentation network, has been widely used in many fields, such as intelligent robots, security, and autonomous driving. However, these Convolutional Neural Network (CNN)-based networ...
Fine-grained food image segmentation is essential for accurate dietary assessment and nutritional analysis, yet remains highly challenging due to ambiguous boundaries, inter-class similarity, and dense layouts of meals containing many different ingre...
Accurate object detection and an understanding of the surroundings are key requirements when applying computer vision systems in the automotive or robotics industries, namely with autonomous vehicles or self-driving robots. A precise understanding of...
Accurate retinal vessel segmentation is important for quantitative assessment of vascular abnormalities in retinopathy of prematurity (ROP), but remains challenging because vessels vary greatly in caliber and spatial extent, while thin branches and c...
In smart city contexts, traditional methods for semantic segmentation are affected by adverse conditions, such as rain, fog, or darkness. One challenge is the limited availability of semantic segmentation datasets, specifically for autonomous driving...
Although deep learning has significantly advanced brain tumor MRI segmentation and preoperative planning, existing methods like U-Net and Transformer, which are widely used Encoder–Decoder architectures in medical image segmentation, still have...
Crop segmentation is an important task in Precision Agriculture, where the use of aerial robots with an on-board camera has contributed to the development of new solution alternatives. We address the problem of fig plant segmentation in top-view RGB...
Early detection and analysis of lung cancer involve a precise and efficient lung nodule segmentation in computed tomography (CT) images. However, the anonymous shapes, visual features, and surroundings of the nodules as observed in the CT images pose...
Semantic segmentation of large-scale outdoor 3D LiDAR point clouds becomes essential to understand the scene environment in various applications, such as geometry mapping, autonomous driving, and more. With an advantage of being a 3D metric space, 3D...
Optical coherence tomography (OCT)-based retinal imagery is often utilized to determine influential factors in patient progression and treatment, for which the retinal layers of the human eye are investigated to assess a patient’s health status...
An accurate whole heart segmentation (WHS) on medical images, including computed tomography (CT) and magnetic resonance (MR) images, plays a crucial role in many clinical applications, such as cardiovascular disease diagnosis, pre-surgical planning,...
Glaucoma is a visual disease that affects millions of people, and early diagnosis can prevent total blindness. One way to diagnose the disease is through fundus image examination, which analyzes the optic disc and cup structures. However, screening p...
Structure health inspection is the way to ensure that structures stay in optimum condition. Traditional inspection work has many disadvantages in dealing with the large workload despite using remote image-capturing devices. This research focuses on i...
Few-shot semantic segmentation aims to learn accurate pixel-level classification from limited annotated samples, a critical capability for real-world applications where data acquisition is expensive or impractical. However, existing methods often str...
The brain is the center of human control and communication. Hence, it is very important to protect it and provide ideal conditions for it to function. Brain cancer remains one of the leading causes of death in the world, and the detection of malignan...
Conventional deep learning methods have shown promising results in the medical domain when trained on accurate ground truth data. Pragmatically, due to constraints like lack of time or annotator inexperience, the ground truth data obtained from clini...
Breast cancer accounted for approximately 2.4 million new cases and 694,000 deaths worldwide in 2024. Deep learning approaches, particularly encoder–decoder architectures, have been investigated for mammographic mass segmentation. However, eval...
Accurate, non-invasive body weight estimation is essential for management and performance monitoring in beef cattle systems, yet conventional scales and manual measurements require animal handling, infrastructure, and labor. This study presents an in...
Efficient segmentation of Magnetic Resonance (MR) brain tumor images is of the utmost value for the diagnosis of tumor region. In recent years, advancement in the field of neural networks has been used to refine the segmentation performance of brain...
Phase contrast (PC) magnetic resonance imaging (MRI) is a primary method used to quantify blood flow. Cerebral blood flow (CBF) is an important hemodynamic parameter to characterize cerebrovascular and neurological diseases. However, a critical step...
Remote sensing semantic and referring segmentation share mask geometry but differ in whether a category vocabulary or a natural-language expression selects the output. UniRS-Seg bridges these interfaces through a shared sparse–dense query compi...
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