36 datasets found

The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans

The Abdominal Multi Organ Segmentation 2022 (AMOS2022) challenge dataset contains 500 CT and 100 MRI scans collected from multiple sites, a wide range of imaging conditions, and...

COVID-19 CT scans and corresponding reports from a university hospital

A multi-view multi-stage and multi-window framework for pulmonary artery segmentation from CT scans

A dataset for kidney and kidney tumor segmentation in contrast-enhanced CT images.

A pediatric dataset of abdominal-visceral CT images from 80 patients, with early arterial contrast injection.

The SARS-COV-2 CT-Scan dataset contains 2482 CT scans in total, where 1252 CT scans are positive for SARS-CoV-2 infection (COVID-19) and 1230 CT scans are from patients that are...

Internal CT dataset with 593 3D labeled lesions, 23,785 2D labeled RECIST slices and 12,856 unlabeled 3D volumes.

The RSNA 2019 Brain CT Hemorrhage Challenge dataset is a dataset of CT scans of the brain used for hemorrhage detection and classification.

The NIH pancreas segmentation dataset contains 82 abdominal CT volumes. The width and height of each volume are 512, while the axial view slice number can vary from 181 to 466....

The proposed model uses integrative CT images for the COVID-19 dataset, including 1521 patients with or without COVID-19 pneumonia and their recorded clinical features epidemic.

The RPLHR-CT dataset is a medium-sized dataset containing real-paired thin-CTs and thick-CTs of 250 patients.

COVID-CT dataset that has been used in this study is publicly available. There are 349 images of COVID-19 collected from 216 patients. The non-COVID-19 data contains 397 samples.

The dataset used for testing the proposed CNN model, containing 18 patients with 8-11 CT scans each.

The dataset used for training and testing the baseline CNN model, containing 379 patients with one CT scan each.

The dataset used for testing the proposed Project & Excite (PE) module for whole-brain segmentation of MRI scans and whole-body segmentation of CT scans.

The dataset used for testing the proposed Project & Excite (PE) module for whole-brain segmentation of MRI scans and whole-body segmentation of CT scans.

The dataset used for training and testing the proposed Project & Excite (PE) module for whole-brain segmentation of MRI scans and whole-body segmentation of CT scans.

A two-stage, coarse-to-fine approach that trains an FCN model to roughly delineate the organs of interest in the first stage and then uses these predictions of the first-stage...

A new automatic algorithm for the diagnosis and grading of sacroiliitis in CT scans as incidental findings, for patients who underwent lower back or abdomen CT scanning as part...