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Imaging segmentation mechanism for rectal tumors using improved U-Net
ObjectiveIn radiation therapy, cancerous region segmentation in magnetic resonance images (MRI) is a critical step. For rectal cancer, the automatic...
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Improving image quality of sparse-view lung tumor CT images with U-Net
BackgroundWe aimed to improve the image quality (IQ) of sparse-view computed tomography (CT) images using a U-Net for lung metastasis detection and...
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RU-Net: skull strip** in rat brain MR images after ischemic stroke with rat U-Net
BackgroundExperimental ischemic stroke models play a fundamental role in interpreting the mechanism of cerebral ischemia and appraising the...
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Clinical Use of Hematoma Volume Based On Automated Segmentation of Chronic Subdural Hematoma Using 3D U-Net
PurposeTo propose a method for calculating hematoma volume based on automatic segmentation of chronic subdural hematoma (CSDH) using 3D U‑net and...
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GA-UNet: A Lightweight Ghost and Attention U-Net for Medical Image Segmentation
U-Net has demonstrated strong performance in the field of medical image segmentation and has been adapted into various variants to cater to a wide...
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An automated in vitro wound healing microscopy image analysis approach utilizing U-net-based deep learning methodology
BackgroundThe assessment of in vitro wound healing images is critical for determining the efficacy of the therapy-of-interest that may influence the...
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Automatic Skeleton Segmentation in CT Images Based on U-Net
Bone metastasis, emerging oncological therapies, and osteoporosis represent some of the distinct clinical contexts which can result in morphological...
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Segmentation of thyroid glands and nodules in ultrasound images using the improved U-Net architecture
BackgroundIdentifying thyroid nodules’ boundaries is crucial for making an accurate clinical assessment. However, manual segmentation is...
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Residual Deformable Split Channel and Spatial U-Net for Automated Liver and Liver Tumour Segmentation
Accurate segmentation of the liver and liver tumour (LT) is challenging due to its hazy boundaries and large shape variability. Although using U-Net...
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Multi-Scale and Spatial Information Extraction for Kidney Tumor Segmentation: A Contextual Deformable Attention and Edge-Enhanced U-Net
Kidney tumor segmentation is a difficult task because of the complex spatial and volumetric information present in medical images. Recent advances in...
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Brain tumour segmentation based on an improved U-Net
BackgroundAutomatic segmentation of brain tumours using deep learning algorithms is currently one of the research hotspots in the medical image...
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The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net
BackgroundTo evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized...
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DI-UNet: dual-branch interactive U-Net for skin cancer image segmentation
PurposeSkin disease is a prevalent type of physical ailment that can manifest in multitude of forms. Many internal diseases can be directly reflected...
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Automated segmentation of craniopharyngioma on MR images using U-Net-based deep convolutional neural network
ObjectivesTo develop a U-Net-based deep learning model for automated segmentation of craniopharyngioma.
MethodsA total number of 264 patients...
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Towards reliable hepatocytic anatomy segmentation in laparoscopic cholecystectomy using U-Net with Auto-Encoder
BackgroundMost bile duct (BDI) injuries during laparoscopic cholecystectomy (LC) occur due to visual misperception leading to the misinterpretation...
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TBUnet: A Pure Convolutional U-Net Capable of Multifaceted Feature Extraction for Medical Image Segmentation
Many current medical image segmentation methods utilize convolutional neural networks (CNNs), with some extended U-Net-based networks relying on deep...
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Generalizable attention U-Net for segmentation of fibroglandular tissue and background parenchymal enhancement in breast DCE-MRI
ObjectivesDevelopment of automated segmentation models enabling standardized volumetric quantification of fibroglandular tissue (FGT) from native...
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Automated lung cancer assessment on 18F-PET/CT using Retina U-Net and anatomical region segmentation
ObjectivesTo develop and test a Retina U-Net algorithm for the detection of primary lung tumors and associated metastases of all stages on FDG-PET/CT.
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Automatic Liver Segmentation Using EfficientNet and Attention-Based Residual U-Net in CT
This paper proposes a new network framework, which leverages EfficientNetB4, attention gate, and residual learning techniques to achieve automatic...
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ChimeraNet: U-Net for Hair Detection in Dermoscopic Skin Lesion Images
Hair and ruler mark structures in dermoscopic images are an obstacle preventing accurate image segmentation and detection of critical network...
