Computed Tomography (CT)

169 papers with code • 0 benchmarks • 13 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 )

Libraries

Use these libraries to find Computed Tomography (CT) models and implementations

Most implemented papers

COVID-CT-Dataset: A CT Scan Dataset about COVID-19

UCSD-AI4H/COVID-CT 30 Mar 2020

Using this dataset, we develop diagnosis methods based on multi-task learning and self-supervised learning, that achieve an F1 of 0. 90, an AUC of 0. 98, and an accuracy of 0. 89.

MULAN: Multitask Universal Lesion Analysis Network for Joint Lesion Detection, Tagging, and Segmentation

rsummers11/CADLab 12 Aug 2019

When reading medical images such as a computed tomography (CT) scan, radiologists generally search across the image to find lesions, characterize and measure them, and then describe them in the radiological report.

UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation

MrGiovanni/UNetPlusPlus 11 Dec 2019

The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN).

The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

neheller/kits19 31 Mar 2019

The morphometry of a kidney tumor revealed by contrast-enhanced Computed Tomography (CT) imaging is an important factor in clinical decision making surrounding the lesion's diagnosis and treatment.

The Liver Tumor Segmentation Benchmark (LiTS)

assassint2017/MICCAI-LITS2017 13 Jan 2019

The best liver segmentation algorithm achieved a Dice score of 0. 96(MICCAI) whereas for tumor segmentation the best algorithm evaluated at 0. 67(ISBI) and 0. 70(MICCAI).

Disentangled Representation Learning in Cardiac Image Analysis

agis85/anatomy_modality_decomposition 22 Mar 2019

We can venture further and consider that a medical image naturally factors into some spatial factors depicting anatomy and factors that denote the imaging characteristics.

Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT

hanyoseob/framing-u-net 28 Aug 2017

X-ray computed tomography (CT) using sparse projection views is a recent approach to reduce the radiation dose.

Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNIT

simontomaskarlsson/GAN-MRI 20 Jun 2018

Here, we evaluate two unsupervised GAN models (CycleGAN and UNIT) for image-to-image translation of T1- and T2-weighted MR images, by comparing generated synthetic MR images to ground truth images.