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Most existing brain tumor segmentation methods leverage multimodal Magnetic Resonance Imaging (MRI) to achieve high segmentation performance. However, the lack of complete image modalities in clinical practice usually leads to a significant decline in ...

• We employ U-Net as the framework and refine the generated point cloud features using a cross-attention mechanism at multiple-resolutions. It first generates the object's contour, and then the complete point cloud with rich texture features ...

In the field of 3D vision, 3D point cloud completion is a crucial task in many practical applications. Current methods use Transformer's Encoder-Decoder framework to predict the missing part of the point cloud features at low resolution, which ...

Pathological myopia can lead to a series of eye diseases, including glaucoma and retinal pathologies. One of its most significant changes is the alteration in the size of the optic disc area in fundus images. Therefore, precise segmentation of ...

• Propose a fully convolutional medical image segmentation network with efficient small-kernel convolutions.
• Introduce the Multi-Layer Cross-Attention Gate to effectively merge features from different levels.
• Expand the optic disc ...

Deformable image registration minimizes the discrepancy between moving and fixed images by establishing linear and nonlinear spatial correspondences. It plays a crucial role in surgical navigation, image fusion and disease analysis. Its challenge ...

The segmentation of teeth and root canals in oral Cone Beam Computed Tomography (CBCT) images provides crucial diagnostic value for diseases. However, existing methods have not effectively addressed the challenge of accurately segmenting teeth, ...$$$$

Accurate medical image segmentation of lesion areas is crucial in assisting diagnostics and treatment planning of diseases. In this paper, we propose a boundary-guided context fusion U-Net(BCF-UNet) for medical image segmentation to address a ...

Medical images are often acquired in different settings, requiring harmonization to adapt to the operating point of algorithms. Specifically, to standardize the physical spacing of imaging voxels in heterogeneous inference settings, images are ...

Medical image segmentation is crucial in the field of medical imaging, assisting healthcare professionals in analyzing images and improving diagnostic performance. Recent advancements in Transformer-based networks, which utilize self-attention ...

The rat sciatic nerve model is commonly used to test novel therapies for nerve injury repair. The static sciatic index (SSI) is a useful metric for quantifying functional recovery, and involves comparing an operated paw versus a control paw using ...

• Open-source toolbox that streamlines the acquisition of the metric SSI to test novel peripheral nerve injury therapies.
• Performing the SSI manually is labour intensive and prone to observer bias, incumbering the discovery of novel ...

Road cracks pose a persistent challenge in road maintenance, with timely detection and repair crucial for enhancing road safety. However, determining which cracks require repair can be difficult, necessitating a quantitative analysis approach. ...

Multi-channel speech enhancement plays a critical role in numerous speech-related applications. Several previous works explicitly utilize deep neural networks (DNNs) to exploit tempo-spectral signal characteristics, which often leads to excellent ...

In the process of segmentation of non-small cell lung cancer, rapid and accurate segmentation methods have always been the focus and difficulty of research. U-Net and its variants occupy a pivotal position in the realm of medical segmentation. Based on U-...

The traditional Selective Kernel Network cannot deeply explore the multidimensional relationship between features. To handle the problem, this paper design a novel attention mechanism, which fuses the spatial attention and channel attention. And we design ...

During the integrated circuit design process, the maximum IR drop value is often given more attention. The frequency of the maximum IR drop in the actual circuits presents an uneven dispersion, i.e., long-tail distribution. To address this problem, this ...

The study and prediction of space weather entails the analysis of solar images showing structures of the Sun’s atmosphere. When imaged from the Earth’s ground, images may be polluted by terrestrial clouds which hinder the detection of solar ...

Breast ultrasound imaging, due to its noninvasive nature and cost-effectiveness, has become an indispensable instrument in the early detection of breast cancer, highlighting the importance of early detection of lesions for timely intervention. In ...$\mathrm{}$

Rare genetic diseases are difficult to diagnose and this translates in patient's diagnostic odyssey! This is particularly true for more than 900 rare diseases including orodental developmental anomalies such as missing teeth. However, if left ...

• Rare genetic diseases are difficult to diagnose leading to patient's diagnostic odyssey!.
• The i-Dent project conceived a pre-diagnostic tool to detect rare diseases with tooth agenesis.
• To identify missing teeth on X-rays, image ...

The design and placement of a new building in an urban environment is optimised so that pedestrians do not feel uncomfortable due to high wind speeds. Architects and city planners typically use Computational Fluid Dynamics (CFD) simulations to ...

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• Interactive design framework for new buildings in existing city structures.
• 2D-U-Nets for limited RANS simulations to predict wind velocity and pressure.
• Optimising building parameters to reduce high velocities in existing city ...

Single Image Super-Resolution (SISR) is a fundamental computer vision task aimed at enhancing the spatial resolution and quality of low-resolution images. In recent years, deep learning techniques have revolutionized the field of SISR, enabling ...

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• A U-shaped deep net with 150 layers called Deep RegNet-150 is presented for the SISR.
• The 150 layers of the deep regression network are modelled with a Residual Channel Attention Block (RCAB).
• The down sampling portion of U-Net has ...

The availability of comprehensive datasets is a crucial challenge for developing artificial intelligence (AI) models in various applications and fields. The lack of large and diverse public fabric defect datasets forms a major obstacle to properly ...