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VascuConNet: an enhanced connectivity network for vascular segmentation
Medical image segmentation commonly involves diverse tissue types and structures, including tasks such as blood vessel segmentation and nerve fiber...
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Efficient skin lesion segmentation with boundary distillation
AbstractMedical image segmentation models are commonly known for their complex structures, which often render them impractical for use on edge...
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Semantic Polyp Generation for Improving Polyp Segmentation Performance
PurposeTo improve the performance of deep-learning-based image segmentation, a sufficient amount of training data is required. However, it is more...
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Segmentation and Volumetric Analysis of Heart from Cardiac CT Images
PurposeCardiac CT is a valuable diagnostic tool in evaluating cardiovascular diseases. Accurate segmentation of the heart and its structures from...
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Parotid Gland Segmentation Using Purely Transformer-Based U-Shaped Network and Multimodal MRI
Parotid gland tumors account for approximately 2% to 10% of head and neck tumors. Segmentation of parotid glands and tumors on magnetic resonance...
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Uncertainty-guided transformer for brain tumor segmentation
AbstractMulti-model data can enhance brain tumor segmentation for the rich information it provides. However, it also introduces some redundant...
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Deep Upscale U-Net for automatic tongue segmentation
AbstractIn a treatment or diagnosis related to oral health conditions such as oral cancer and oropharyngeal cancer, an investigation of tongue’s...
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Enhanced Spatial Fuzzy C-Means Algorithm for Brain Tissue Segmentation in T1 Images
Magnetic Resonance Imaging (MRI) plays an important role in neurology, particularly in the precise segmentation of brain tissues. Accurate...
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A lightweight bladder tumor segmentation method based on attention mechanism
AbstractIn the endoscopic images of bladder, accurate segmentation of different grade bladder tumor from blurred boundary regions and highly variable...
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Segmentation of the left atrial appendage based on fusion attention
AbstractIn clinical practice, the morphology of the left atrial appendage (LAA) plays an important role in the selection of LAA closure devices for...
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Aorta Segmentation in 3D CT Images by Combining Image Processing and Machine Learning Techniques
PurposeAorta segmentation is extremely useful in clinical practice, allowing the diagnosis of numerous pathologies, such as dissections, aneurysms...
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MCAFNet: multiscale cross-layer attention fusion network for honeycomb lung lesion segmentation
Accurate segmentation of honeycomb lung lesions from lung CT images plays a crucial role in the diagnosis and treatment of various lung diseases....
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Adversarial attacks and adversarial training for burn image segmentation based on deep learning
AbstractDeep learning has been widely applied in the fields of image classification and segmentation, while adversarial attacks can impact the...
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An automatic progressive chromosome segmentation approach using deep learning with traditional image processing
The fully automatic chromosome analysis system plays an important role in the detection of genetic diseases, which in turn can reduce the diagnosis...
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Skip connection information enhancement network for retinal vessel segmentation
AbstractMany major diseases of the retina often show symptoms of lesions in the fundus of the eye. The extraction of blood vessels from retinal...
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Automatic segmentation of brain glioma based on XY-Net
Glioma is a malignant primary brain tumor, which can easily lead to death if it is not detected in time. Magnetic resonance imaging is the most...
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Segmentation of liver and liver lesions using deep learning
Segmentation of organs and lesions could be employed for the express purpose of dosimetry in nuclear medicine, assisted image interpretations, and...
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Phytoplankton Image Segmentation and Annotation Method Based on Microscopic Fluorescence
Microscopic phytoplankton segmentation is an important part of water quality assessment. The segmentation of microscopic phytoplankton still faces...
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Visual Prompting Based Incremental Learning for Semantic Segmentation of Multiplex Immuno-Flourescence Microscopy Imagery
Deep learning approaches are state-of-the-art for semantic segmentation of medical images, but unlike many deep learning applications, medical...
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Robust thalamic nuclei segmentation from T1-weighted MRI using polynomial intensity transformation
Accurate segmentation of thalamic nuclei, crucial for understanding their role in healthy cognition and in pathologies, is challenging to achieve on...
