Computed Tomography (CT)
294 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
MiM: Mask in Mask Self-Supervised Pre-Training for 3D Medical Image Analysis
We further scale up the MiM to large pre-training datasets with more than 10k volumes, showing that large-scale pre-training can further enhance the performance of downstream tasks.
Differentiable Score-Based Likelihoods: Learning CT Motion Compensation From Clean Images
Motion artifacts can compromise the diagnostic value of computed tomography (CT) images.
PEMMA: Parameter-Efficient Multi-Modal Adaptation for Medical Image Segmentation
In this work, we propose a parameter-efficient multi-modal adaptation (PEMMA) framework for lightweight upgrading of a transformer-based segmentation model trained only on CT scans to also incorporate PET scans.
RadRotator: 3D Rotation of Radiographs with Diffusion Models
This transformation makes the diffusion model agnostic to any distribution variations of the input data pixel intensity, enabling the reliable training of a DL model on input DRRs and applying the exact same model to conventional radiographs (or DRRs) during inference.
Unlocking Robust Segmentation Across All Age Groups via Continual Learning
Most deep learning models in medical imaging are trained on adult data with unclear performance on pediatric images.
Multi-target and multi-stage liver lesion segmentation and detection in multi-phase computed tomography scans
Although this approach utilizes information from all the phases and outperform single-phase segmentation networks, we demonstrate that their performance is not optimal and can be further improved by incorporating the learning from models trained on each single-phase individually.
Reconstructing classes of 3D FRI signals from sampled tomographic projections at unknown angles
By using the divergence theorem, we are able to retrieve the projected vertices of the polyhedron from the sampled tomographic projections, and then we show how to retrieve the 3D object and the projection angles from this information.
Multi-Branch Generative Models for Multichannel Imaging with an Application to PET/CT Joint Reconstruction
This paper presents a proof-of-concept approach for learned synergistic reconstruction of medical images using multi-branch generative models.
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.
