225 datasets found

Abdominal organ segmentation in 20 test ceCT scans of the Visceral dataset.

Three challenging medical applications: whole-brain, whole-body and retinal layer segmentation.

The dataset contains 3D thoracic CT scans for segmentation of thoracic organs.

Acquiring and annotating sufficient labeled data is crucial in developing accurate and robust learning-based models, but obtaining such data can be challenging in many medical...

The V-Net is a deep learning model for medical image segmentation that uses a U-Net architecture.

The 3D Unet is a deep learning model for medical image segmentation that uses a U-Net architecture.

The R2U-Net is a deep learning model for medical image segmentation that combines U-Net, ResNet, and recurrent neural network (RCNN).

The Synapse multi-organ dataset is a collection of 30 abdominal CT scans with corresponding segmentation masks.

Skin lesion segmentation dataset

A dataset for medical image segmentation with limited annotations

Four medical image segmentation datasets, covering various imaging modalities such as colonoscopy, dermoscopy, and microscopy.

Optic disc and cup segmentation dataset used for evaluating the efficiency of the proposed Translation Variant Convolution (TVConv) operator.

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 COSMOS 553K dataset is a large-scale medical image segmentation dataset.

A dataset for the development and evaluation of segmentation algorithms for various medical imaging modalities.

A large annotated medical image dataset for the development and evaluation of segmentation algorithms.

The dataset used for medical image segmentation tasks, including multi-organ segmentation and few-shot learning.

The nnu-net dataset

The dataset used for prostate cross-site segmentation

Three medical image segmentation datasets: DRIVE, STARE, and CHASH_DB1.