211 datasets found

Two cardiac computed tomography (CT) datasets consisting of 760 volumes across the whole cardiac cycle from 39 patients, and of 60 volumes from 60 patients respectively were...

A dataset for multi-organ segmentation problem, filling the gap of extendable multi-domain learning in image segmentation.

The dataset used in the paper is a real-world dataset for testing the parametric efficiency of quadratic networks.

The dataset used for medical image segmentation.

The dataset used for medical image segmentation.

The dataset used for medical image segmentation.

The dataset used for polyp segmentation in endoscopic images, gland segmentation in histology images, breast cancer segmentation in ultrasound images, and fluid region...

The dataset used for vestibular schwannoma MRI segmentation

The dataset used in this paper is a collaborative filtering dataset, specifically the Million Song Dataset (MSD), which contains listening counts from 1 million users on 50,000...

The MSD Spleen dataset consists of n = 61 portal-venous phase contrast-enhanced abdominal CT scans, out of which n = 41 (67%) contained annotations for the spleen.

The MSD Liver dataset consists of n = 201 portal-venous phase contrast-enhanced abdominal CT scans, out of which n = 131 (65%) contained annotations for the liver and liver tumors.

The Medical Segmentation Decathlon (MSD) is a collection of 10 benchmark datasets for segmentation spanning different body parts and modalities.

Semantic segmentation for medical 3D image stacks enables accurate volumetric reconstructions, computer-aided diagnostics and follow up treatment planning.

The dataset used in this paper for whole heart segmentation task

Macular hole segmentation dataset using 3D U-Net architecture

The RIGA+ dataset is a multi-domain joint optic disc (OD) / optic cup (OC) segmentation dataset annotated by six ophthalmologists.

Multi-organ medical image segmentation is crucial for diagnosis and treatment planning. However, many methods including variability in different factors complicate the task,...

Automatic segmentation of brain tumors is an essential but challenging step for extracting quantitative imaging biomarkers for accurate tumor detection, diagnosis, prognosis,...

Spleen CT segmentation dataset

Brain tumour MRI segmentation dataset