12 datasets found

Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: Is the problem solved?

A vertebral labeling and segmentation benchmark for multi-detector CT images.

The DRIVE dataset contains the curvilinear-shaped vessel. This dataset consists of 40 565 × 584 color retinal images, which are split into 20 training images and 20 test images.

The BraTS’19 dataset is used for testing the proposed FNOSeg3D model. It contains 335 cases of gliomas, each with four modalities of T1, post-contrast T1, T2, and T2-FLAIR...

The dataset in this paper comes from the database of the Ultrasound Imaging Department of Peking University Shenzhen Hospital.

The CVC-ClinicDB dataset is a collection of 612 images, each with a resolution of 384x288, used for polyp diagnosis within colonoscopy videos.

The BUSI dataset is a collection of ultrasound images depicting normal, benign, and malignant breast cancer cases along with their corresponding segmentation maps.

The dataset used in the paper for medical image segmentation tasks.

The dataset used in the paper for medical image segmentation tasks.

The ClinicDB dataset comprises 100 images for testing.

The MoNuSeg dataset comprises 30 microscopic images from seven organs in the training set, with annotations of 21,623 individual nuclei, and 14 similar images in the test set.

The CHASE_DB1 dataset is a publicly available dataset for retinal vessel segmentation.