Search Results - "Medical image segmentation"

As artificial intelligence advances in medical image analysis, its environmental impact remains largely overlooked. This study analyzes the energy demands of AI workflows for medical image segmentation using the popular Kidney Tumor Segmentation-2019 (KiTS-19) dataset. It examines how training and i...

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The rapid advancement of prompt-based models in natural language processing and image generation has revolutionized the field of image segmentation. The introduction of the Segment Anything Model (SAM) has further invigorated this domain with its unprecedented versatility. However, its applicability...

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To address the widespread challenges of significant multi-category dental morphological variations and interference from overlapping anatomical structures in panoramic dental X-ray images, this paper proposes a dual-stream dental segmentation model based on Transformer heterogeneous feature compleme...

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Abdominal organ segmentation in CT images is crucial for accurate diagnosis, treatment planning, and condition monitoring. However, the annotation process is often hindered by challenges such as low contrast, artifacts, and complex organ structures. While unsupervised domain adaptation (UDA) has sho...

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Semantic segmentation is an exciting research topic in medical image analysis because it aims to detect objects in medical images. In recent years, approaches based on deep learning have shown a more reliable performance than traditional approaches in medical image segmentation. The U-Net network i...

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With the continuous advancement of deep learning technologies, deep learning-based medical image segmentation methods have achieved remarkable results. However, existing segmentation approaches still face several key challenges, including the insufficient extraction of local and global information f...

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<italic>Goal:</italic> In recent years, deep neural networks have consistently outperformed previously proposed methods in the domain of medical segmentation. However, due to their nature, these networks often struggle to delineate desired structures in data that fall outside their train...

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Diffusion Tensor Imaging (DTI) is a crucial imaging technique for accurately assessing pathological changes in Cervical Spondylotic Myelopathy (CSM). However, the segmentation of spinal cord DTI images primarily relies on manual methods, which are labor-intensive and heavily dependent on the subject...

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Accurate medical image segmentation plays a critical role in clinical diagnosis, treatment planning, and a wide range of healthcare applications. Although U-shaped CNNs and Transformer-based architectures have shown promise, CNNs struggle to capture long-range dependencies, whereas Transformers suff...

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Accurate and generalizable segmentation of medical images remains a challenging task due to boundary ambiguity and variations across domains. In this paper, an implicit transformer framework with a high-frequency adapter for medical image segmentation (HiImp-SMI) is proposed. A new dual-branch archi...

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Dental caries, also named tooth decay, is a major issue for oral health and is caused by bacteria in dental plaque. Detecting caries early on is essential for preventing further damage. Because caries are often small, they can lead to unnecessary treatments or missed diagnoses. This study tackles t...

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Medical image segmentation, a critical task in medical image analysis, aims to precisely delineate regions of interest (ROIs) such as organs, lesions, and cells, and is crucial for applications including computer-aided diagnosis, surgical planning, radiation therapy, and pathological analysis. While...

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<b>Background:</b> Integrating artificial intelligence into clinical photography offers great potential for monitoring skin conditions such as actinic keratosis (AK) and skin field cancerization. Identifying the extent of AK lesions often requires more than analyzing lesion morphology—it...

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Medical image segmentation is essential for clinical diagnosis and treatment planning. Existing segmentation methods encounter challenges such as managing size variations, interpreting contextual relationships, and integrating multi-source data. This paper introduces SLGMA-UNet, an enhanced architec...

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IntroductionAccurate segmentation of 3D medical images is crucial for clinical diagnosis and treatment planning. Traditional CNN-based methods effectively capture local features but struggle with modeling global contextual dependencies. Recently, transformer-based models have shown promise in captur...

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Endoscopic image segmentation plays a critical role in diagnosing early gastrointestinal tumors, which is essential for preventing colorectal and gastric cancers. However, achieving accurate segmentation is challenging due to issues such as boundary blurring, low contrast, and small lesion sizes. Th...

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Surgical image segmentation serves as the foundation for laparoscopic surgical navigation technology. The indistinct local features of biological tissues in laparoscopic image pose challenges for image segmentation. To address this issue, we develop an image segmentation network tailored for laparos...

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Medical imaging is a critical tool for diagnosing and treating various diseases such as Chronic Obstructive Pulmonary Disease (COPD), tuberculosis, lung cancer, and Coronavirus. Techniques such as X-rays, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (...

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Accurate segmentation of the prostate in T2-weighted MRI is critical for effective prostate diagnosis and treatment planning. Existing methods often struggle with the complex textures and subtle variations in the prostate. To address these challenges, we propose RaNet (Residual Attention Network), a...

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<b>Background</b>: Magnetic Resonance Imaging (MRI) provides rich tumor information through different imaging modalities (T1, T1ce, T2, and FLAIR). Each modality offers distinct contrast and tissue characteristics, which help in the more comprehensive identification and analysis of tumor...

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