12 datasets found

The LITS MICCAI-2017 challenge dataset is used for training and testing the proposed 2-stage cascaded model for segmentation of liver and its tumors in CT images.

A proprietary spinal metastasis CT dataset used for evaluating the proposed learning-based bone quality classification method.

Liver and tumor segmentation from Computed Tomography (CT) images is a mandatory task in diagnosing, monitoring, and treating liver diseases.

Automated liver tumor segmentation from Computed Tomography (CT) images is a necessary prerequisite in the interventions of hepatic abnormalities and surgery planning.

The MP-ACT dataset is a multi-phase abdomen CT dataset containing 164 pairs of non-contrast and arterial phase images, as well as 170 pairs of non-contrast and venous phase images.

A dataset of 20 fully annotated clinical CTs of the hip and thigh regions, and 18 partially annotated CTs from The Cancer Imaging Archive (TCIA) database.

COVID-19 CT images of the lungs, ground glass turbidity is the most common finding that requires specialist diagnosis.

The CRC-LNM dataset includes 52 CT images of 145 lymph nodes with confirmed metastasis through both pathology and imaging.

The ABD-LN dataset comprises 86 CT images from 86 patients, totaling 595 identified abdominal lymph nodes.

Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images.

The DeepLesion dataset is a large-scale dataset containing measurements and 2D bounding-boxes of over 32K lesions from a variety of body parts on computed tomography (CT) images.

The dataset used for COVID-19 segmentation from CT images, using deep learning and imaging for delineating COVID-19 infection in lungs.