Computed Tomography (CT)
295 papers with code • 0 benchmarks • 14 datasets
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 )
Benchmarks
These leaderboards are used to track progress in Computed Tomography (CT)
Libraries
Use these libraries to find Computed Tomography (CT) models and implementationsDatasets
LUNA
PadChest
LUNA16
ChestX-ray8
LiTS17
COVID-CT
MosMedData
BIMCV COVID-19
CC-19
Latest papers with no code
Deep-learning Segmentation of Small Volumes in CT images for Radiotherapy Treatment Planning
We applied specific strategies to improve the segmentation accuracy of the small volumes in this anatomical region, i. e., the lens of the eye.
Bi-level Guided Diffusion Models for Zero-Shot Medical Imaging Inverse Problems
A central challenge in this approach, however, is how to guide an unconditional prediction to conform to the measurement information.
Detection of subclinical atherosclerosis by image-based deep learning on chest x-ray
A deep-learning algorithm to predict coronary artery calcium (CAC) score (the AI-CAC model) was developed on 460 chest x-ray (80% training cohort, 20% internal validation cohort) of primary prevention patients (58. 4% male, median age 63 [51-74] years) with available paired chest x-ray and chest computed tomography (CT) indicated for any clinical reason and performed within 3 months.
H2ASeg: Hierarchical Adaptive Interaction and Weighting Network for Tumor Segmentation in PET/CT Images
However, modality-specific encoders used in these methods operate independently, inadequately leveraging the synergistic relationships inherent in PET and CT modalities, for example, the complementarity between semantics and structure.
Towards Automatic Abdominal MRI Organ Segmentation: Leveraging Synthesized Data Generated From CT Labels
Our approach shows comparable results compared to fully-supervised segmentation methods trained on MR data.
COVID-19 detection from pulmonary CT scans using a novel EfficientNet with attention mechanism
Manual analysis and diagnosis of COVID-19 through the examination of Computed Tomography (CT) images of the lungs can be time-consuming and result in errors, especially given high volume of patients and numerous images per patient.
Mitigating Data Consistency Induced Discrepancy in Cascaded Diffusion Models for Sparse-view CT Reconstruction
This study introduces a novel Cascaded Diffusion with Discrepancy Mitigation (CDDM) framework, including the low-quality image generation in latent space and the high-quality image generation in pixel space which contains data consistency and discrepancy mitigation in a one-step reconstruction process.
Monocular Microscope to CT Registration using Pose Estimation of the Incus for Augmented Reality Cochlear Implant Surgery
For those experiencing severe-to-profound sensorineural hearing loss, the cochlear implant (CI) is the preferred treatment.
Learned 3D volumetric recovery of clouds and its uncertainty for climate analysis
We demonstrate the approach in simulations and on real-world data, and indicate the relevance of 3D recovery and uncertainty to precipitation and renewable energy.
A Data Augmentation Pipeline to Generate Synthetic Labeled Datasets of 3D Echocardiography Images using a GAN
A quantitative analysis of the 3D segmentations given by the models trained with the synthetic images indicated the potential use of this GAN approach to generate 3D synthetic data, use the data to train DL models for different clinical tasks, and therefore tackle the problem of scarcity of 3D labeled echocardiography datasets.
