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Computed Tomography (CT)

The term “computed tomography”, or CT, refers to a computerized x-ray imaging procedure in which a narrow beam of x-rays is aimed at a patient and quickly rotated around the body, producing signals that are processed by the machine's computer to generate cross-sectional images—or “slices”—of the body.

( Image credit: Liver Lesion Detection from Weakly-labeled Multi-phase CT Volumes with a Grouped Single Shot MultiBox Detector )

Papers

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Showing 776–800 of 1207 papers

TitleStatusHype
Total Variation Regularization for Tomographic Reconstruction of Cylindrically Symmetric Objects—0
Towards Automatic Abdominal MRI Organ Segmentation: Leveraging Synthesized Data Generated From CT Labels—0
Towards Head Computed Tomography Image Reconstruction Standardization with Deep Learning Assisted Automatic Detection—0
Towards Patient-Specific Surgical Planning for Bicuspid Aortic Valve Repair: Fully Automated Segmentation of the Aortic Valve in 4D CT—0
Towards radiologist-level cancer risk assessment in CT lung screening using deep learning—0
Towards Transfer Learning for Large-Scale Image Classification Using Annealing-based Quantum Boltzmann Machines—0
Trainable Joint Bilateral Filters for Enhanced Prediction Stability in Low-dose CT—0
Training of deep cross-modality conversion models with a small dataset, and their application in megavoltage CT to kilovoltage CT conversion—0
Transfer Learning by Cascaded Network to identify and classify lung nodules for cancer detection—0
Transfer learning for multi-center classification of chronic obstructive pulmonary disease—0
Transformers for CT Reconstruction From Monoplanar and Biplanar Radiographs—0
Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network—0
Quantifying uncertainty in lung cancer segmentation with foundation models applied to mixed-domain datasets—0
TSUBF-Net: Trans-Spatial UNet-like Network with Bi-direction Fusion for Segmentation of Adenoid Hypertrophy in CT—0
TV-regularized CT Reconstruction and Metal Artifact Reduction Using Inequality Constraints with Preconditioning—0
Two Stage Segmentation of Cervical Tumors using PocketNet—0
Sequential Diffusion-Guided Deep Image Prior For Medical Image Reconstruction—0
Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data—0
UMedNeRF: Uncertainty-aware Single View Volumetric Rendering for Medical Neural Radiance Fields—0
Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction using the Local Lipschitz—0
Uncertainty-Guided Coarse-to-Fine Tumor Segmentation with Anatomy-Aware Post-Processing—0
Underestimation of lung regions on chest X-ray segmentation masks assessed by comparison with total lung volume evaluated on computed tomography—0
UNet-3D with Adaptive TverskyCE Loss for Pancreas Medical Image Segmentation—0
U-Net Based Architecture for an Improved Multiresolution Segmentation in Medical Images—0
U-Net in Medical Image Segmentation: A Review of Its Applications Across Modalities—0
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