22 datasets found

A dataset of brain metastases (BM) segmentation, including T1 contrast enhanced (T1CE) MRI datasets.

A dataset of brain metastases (BM) segmentation, including T1 contrast enhanced (T1CE) MRI datasets.

A large-scale microscope image dataset of Trichomonas vaginalis for segmentation, consisting of 3,158 images with high-quality annotations.

3D X-ray microscope data for 3D object detection, segmentation, and metrology for buried structures in advanced IC packages

The Spot-the-difference self-supervised pre-training for anomaly detection and segmentation dataset.

The MOTS20 dataset is used for multi-object tracking and segmentation.

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

The dataset used for the challenging task of volumetric whole brain segmentation.

The dataset used for the challenging task of volumetric whole brain segmentation.

The dataset used for cardiac MRI segmentation

The Cell Tracking Challenge is a benchmark for cell tracking and segmentation. It consists of several datasets, including Fluo-N2DH-SIM+, DIC-C2DL-HeLa, PhC-C2DH-U373,...

Whole brain segmentation using deep learning (DL) is a very challenging task since the number of anatomical labels is very high compared to the number of available training images.

The ACDC dataset is a large-scale dataset containing images of urban scenes under different weather conditions.

The EM segmentation dataset has 30 training images with publicly available groundtruth and 30 test images with annotations kept private for the assessment of segmentation accuracy.

A dataset of brain MRI images for evaluation of segmentation algorithms.

A dataset of multiple sclerosis lesions for evaluation of segmentation algorithms.

A dataset of multiparametric MRI images of cervical cancer patients for segmentation and analysis.

Segmenting and generating the invisible.

More challenging MOTS dataset with higher instance density and crowded scenes.

Multi-object tracking and segmentation (MOTS) dataset, with dense instance segment annotations.