211 datasets found

The GlaS dataset contains 165 H&E-stained histopathology patches extracted from 16 WSIs.

The 2018 Data Science Bowl challenge dataset is used for nuclei cell image segmentation.

Liver Tumor Segmentation dataset contains images of liver tumors from CT scans. The dataset is used for automatic segmentation of liver tumors.

Retinal Images vessel Tree Extraction dataset contains images of retinal vessels from fundus images. The dataset is used for automatic extraction of retinal vessels.

Gland Segmentation dataset contains images of glands from microscopic histology slides. The dataset is used for automatic segmentation of glands.

Brain Anatomy Segmentation from US images dataset contains images of brain anatomy from ultrasound scans. The dataset is used for automatic segmentation of brain ventricles and...

The Prostate dataset is a diverse collection of T2-weighted MRI prostate images and corresponding masks, spanning six distinct domains.

Multi-parametric MRI scans from 2000 patients were used for BraTS2021, 1251 of which were provided with segmentation labels to the participants for developing their algorithms,...

A dataset of real animal X-ray images and phantom X-ray images for catheter and guidewire segmentation.

A shape-sensitive loss function for catheter and guidewire segmentation using a vision transformer network.

Medical image segmentation dataset

Medical image segmentation dataset

The Medical Segmentation Decathlon dataset is a benchmark for medical image segmentation, containing 131 CT volumes.

Polyp Segmentation in Endoscopic Images dataset, RETOUCH dataset

Nuclei Segmentation dataset for medical image segmentation.

Lung Segmentation dataset for medical image segmentation.

Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled data is important in many clinical applications. In recent years, deep learning-based...

Medical image segmentation dataset with multiple annotations and uncertainty quantification

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used CIFAR-10, CIFAR-100, ImageNet, CUB-200-2011, and Stanford Dogs datasets.

Four image datasets for medical image segmentation: thyroid nodule, liver and kidney, breast lesion, and skin lesion.