211 datasets found

Fundus and Prostate image segmentation datasets

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.

The MICCAI BRATS dataset is a fully-annotated dataset for brain tumor segmentation. It contains 220 high-grade subjects and 54 low-grade subjects with four modalities: T1, T1c,...

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 dataset used for training and testing the DeepMediX model, which consists of brain MRI and skin cancer images.

The Medical Segmentation Decathlon (MSD) - prostate dataset comprises of 48 (training =32, testing =16) multimodal (T2, ADC) 3D MRI samples.

A general model for medical image segmentation that can solve unknown medical image segmentation tasks without requiring additional training.

Pancreas tumor dataset is a sub-dataset of the Medical Segmentation Decathlon (MSC) MICCAI 2018 challenge.

Bladder tumor dataset contains 2200 MRI slices from 25 patients with pathologically confirmed bladder cancer.

This study employs a diverse range of post-processed, multi-annotated medical image segmentation datasets, which exhibit significant variations in the ground truth, to train and...

This study employs a diverse range of post-processed, multi-annotated medical image segmentation datasets, which exhibit significant variations in the ground truth, to train and...

The QUBIQ dataset is a multi-rater, multi-center, multi-modality dataset that includes both 2D and 3D segmentation tasks. It features various modalities such as MRI and CT;...

The TVUS dataset is used for evaluating the proposed method.

The thigh muscle MRI dataset is a dataset for thigh muscle segmentation from MRI images.

The White Blood Cell Image Dataset is a dataset for white blood cell segmentation.

The REFUGE dataset is regarded as the source domain and RIM-ONE-r3 and Drishti-GS datasets are treated as target domains.

Medical image segmentation models are mostly hybrid, which integrate self-attention and convolution layers into the non-isomorphic architecture.