In this study, an advanced semantic segmentation method and deep convolutional neural network was applied to identify the Breast Imaging Reporting and Data System (BI-RADS) lexicon for breast ultrasound images, thereby facilitating image interpretati...
Crop disease leaf image segmentation (CDLIS) is the premise of disease detection, disease type recognition and disease degree evaluation. Various convolutional neural networks (CNN) and their modified models have been provided for CDLIS, but their tr...
In-depth mechanical characterization of veins is required for promising innovations of venous substitutes and for better understanding of venous diseases. Two important physical parameters of veins are shape and thickness, which are quite challenging...
Metallographic image segmentation underpins automated metallographic analysis, yet pixel-level annotation is costly. Metallographic microstructures exhibit dramatic size differences, including cross-scale structures such as large-scale matrix phases,...
The success of deep learning and the segmentation of remote sensing images (RSIs) has improved semantic segmentation in recent years. However, existing RSI segmentation methods have two inherent problems: (1) detecting objects of various scales in RS...
Brain tumor semantic segmentation is a critical medical image processing work, which aids clinicians in diagnosing patients and determining the extent of lesions. Convolutional neural networks (CNNs) have demonstrated exceptional performance in compu...
Cardiac medical image segmentation can advance healthcare and embedded vision systems. In this paper, a symmetric semantic segmentation architecture for cardiac magnetic resonance (MR) images based on a symmetric multiscale detail-guided attention ne...
Medical image segmentation still faces three critical challenges: insufficient joint modeling of local details and long-range dependencies, the high computational burden of transformer-based architectures for high-resolution inputs, and performance d...
This paper describes a new steganographic method for QR codes. Unlike most information-hiding techniques in this field, it does not rely on the error correction property. Instead, it uses the segmentation feature of QR codes. Encoding of data in a QR...
Accurate brain tumor segmentation in both adult and pediatric populations remains a challenge due to substantial differences in brain anatomy, tumor distribution, and subregion size. This study proposes a unified segmentation framework based on nnU-N...
With the swift progress of deep learning and its wide application in semantic segmentation, the effect of semantic segmentation has been significantly improved. However, how to achieve a reasonable compromise between accuracy, model size, and inferen...
Building extraction of remote sensing images is very important for urban planning. In the field of deep learning, in order to extract more detailed building features, more complex convolution operations and larger network models are usually used to s...
Many major diseases can cause changes in the morphology of blood vessels, and the segmentation of retinal blood vessels is of great significance for preventing these diseases. Obtaining complete, continuous, and high-resolution segmentation results i...
Semantic image segmentation has a wide range of applications. When it comes to medical image segmentation, its accuracy is even more important than those of other areas because the performance gives useful information directly applicable to disease d...
Accurate organ-level segmentation is essential for achieving high-throughput, non-destructive, and automated plant phenotyping. To address the challenge of intelligent acquisition of phenotypic parameters in tomato plants, we propose TSINet, an end-t...
Accurate segmentation of lung nodules from pulmonary computed tomography (CT) slices plays a vital role in the analysis and diagnosis of lung cancer. Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in the automatic seg...
Currently, the classification of grapevine black rot disease relies on assessing the percentage of affected spots in the total area, with a primary focus on accurately segmenting these spots in images. Particularly challenging are cases in which lesi...
The segmentation of gastrointestinal (GI) organs is crucial in radiation therapy for treating GI cancer. It allows for developing a targeted radiation therapy plan while minimizing radiation exposure to healthy tissue, improving treatment success, an...
Accurate 3D medical image segmentation requires effective integration of encoder details and decoder semantics. However, their relative contributions vary across decoding stages and spatial locations, and attention based only on the current feature p...
This paper presents LUVS-Net, which is a lightweight convolutional network for retinal vessel segmentation in fundus images that is designed for resource-constrained devices that are typically unable to meet the computational requirements of large ne...
Referring image segmentation aims to segment an object described by a natural language expression from an image. Existing methods perform multi-modal fusion during encoding, typically integrating image and text features before predicting masks via up...
Accurate segmentation of thyroid nodules in ultrasound images is crucial for the diagnosis of thyroid cancer and preoperative planning. However, the segmentation of thyroid nodules is challenging due to their irregular shape, blurred boundary, and un...
To address the issue of insufficient deep contextual information mining in the semantic segmentation task of multiple defects in concrete bridges, due to the diversity in texture, shape, and scale of the defects as well as significant differences in...
With the rapid development of cities, semantic segmentation of urban scenes, as an important and effective imaging method, can accurately obtain the distribution information of typical urban ground features, reflecting the development scale and the l...
The advanced development of deep learning methods has recently made significant improvements in medical image segmentation. Encoder–decoder networks, such as U-Net, have addressed some of the challenges in medical image segmentation with an out...
Video target segmentation is a fundamental problem in computer vision that aims to segment targets from a background by learning their appearance information and movement information. In this study, a video target segmentation network based on the Si...
Transformers have demonstrated a significant advantage over CNNs in modeling long-range dependencies, leading to increasing attention being paid towards their application in semantic segmentation tasks. In the present work, a novel semantic segmentat...
With the rapid development of China’s railways, ensuring the safety of the operating environment of high-speed railways faces daunting challenges. In response to safety hazards posed by light and heavy floating objects during the operation of t...
Semantic segmentation of remote sensing imagery is a pivotal task for intelligent interpretation, with critical applications in urban monitoring, resource management, and disaster assessment. Recent advancements in deep learning have significantly im...
Cracks are vital warning signs to reflect the structural deterioration in concrete constructions and buildings. However, their diverse and complex morphologies make accurate segmentation challenging. Deep learning-based methods effectively alleviate...
Drivable road segmentation aims to sense the surrounding environment to keep vehicles within safe road boundaries, which is fundamental in Advance Driver-Assistance Systems (ADASs). Existing deep learning-based supervised methods are able to achieve...
Cracks are one of the most serious defects that threaten the safety of bridges. In order to detect different forms of cracks in different collection environments quickly and accurately, we proposed a pixel-level crack segmentation network based on co...
In order to improve the accuracy of the segmentation of buildings with small sample sizes, this paper proposes a building-segmentation network, ResFAUnet, with transfer learning and multi-scale feature fusion. The network is based on AttentionUnet. T...
The presence of environmental microorganisms is inevitable in our surroundings, and segmentation is essential for researchers to identify, understand, and utilize the microorganisms; make use of their benefits; and prevent harm. However, the segmenta...
The segmentation of remote sensing images by deep learning technology is the main method for remote sensing image interpretation. However, the segmentation model based on a convolutional neural network cannot capture the global features very well. A...
Superpixels, as essential mid-level image representations, have been widely used in computer vision due to their computational efficiency and redundant compression. Compared with traditional superpixel methods, superpixel algorithms based on deep lea...
Image segmentation is an essential step in image analysis that brings meaning to the pixels in the image. Nevertheless, it is also a difficult task due to the lack of a general suited approach to this problem and the use of real-life pictures that ca...
Prostate cancer is a global health concern, and early diagnosis plays a vital role in improving the survival rate. Accurate segmentation is a key step in the automated diagnosis of prostate cancer; however, manual segmentation remains time-consuming...
The non-destructive study of soil micromorphology via computed tomography (CT) imaging has yielded significant insights into the three-dimensional configuration of soil pores. Precise pore analysis is contingent on the accurate transformation of CT i...
The colonoscopy is the foremost technique for detecting polyps, where accurate segmentation is crucial for effective diagnosis and surgical preparation. Nevertheless, contemporary deep learning-based methods for polyp segmentation face substantial hu...
The accurate estimation of forest area is of paramount importance for carbon sequestration projects, ecotourism and ecological safety. Forest segmentation using remote sensing images is a crucial technique for estimating forest area. However, due to...
Nuclei segmentation and classification are two basic and essential tasks in computer-aided diagnosis of digital pathology images, and those deep-learning-based methods have achieved significant success. Unfortunately, most of the existing studies acc...
Natural imagery segmentation has been transferred to land cover classification in remote sensing imagery with excellent performance. However, two key issues have been overlooked in the transfer process: (1) some objects were easily overwhelmed by the...
Siamese network trackers are a prominent paradigm in visual object tracking due to efficient similarity learning. However, most Siamese trackers are restricted to the bounding box tracking format, which often fails to accurately describe the appearan...
Computer-aided polyp segmentation is a crucial task that supports gastroenterologists in examining and resecting anomalous tissue in the gastrointestinal tract. The disease polyps grow mainly in the colorectal area of the gastrointestinal tract and i...
Satellite remote sensing images contain complex and diverse ground object information and the images exhibit spatial multi-scale characteristics, making the panoptic segmentation of satellite remote sensing images a highly challenging task. Due to th...
Teeth segmentation plays a pivotal role in dentistry by facilitating accurate diagnoses and aiding the development of effective treatment plans. While traditional methods have primarily focused on teeth segmentation, they often fail to consider the b...
Fetal brain tissue segmentation is essential for quantifying the presence of congenital disorders in the developing fetus. Manual segmentation of fetal brain tissue is cumbersome and time-consuming, so using an automatic segmentation method can great...
Pathological diagnosis is considered to be declarative and authoritative. However, reading pathology slides is a challenging task. Different parts of the section are taken and read for different purposes and with different focuses, which further adds...
Product quality inspection is a crucial element of industrial manufacturing, yet flaws such as blemishes and stains frequently emerge after the product is completed. Most research has utilized detection models and avoided segmenting networks due to t...
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