Computed Tomography (CT)

259 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 )


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)

lee-zq/3DUNet-Pytorch 13 Jan 2019

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.

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.